diff --git a/LICENSE b/LICENSE
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--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
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+  16. Limitation of Liability.
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+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    {one line to give the program's name and a brief idea of what it does.}
+    Copyright (C) {year}  {name of author}
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    {project}  Copyright (C) {year}  {fullname}
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.
diff --git a/README.md b/README.md
index d46d5a8..beb1cf8 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,277 @@
-# YOLO_Object_Detection
-This is the code for "YOLO Object Detection" by Siraj Raval on Youtube
+## Intro
+
+[![Build Status](https://travis-ci.org/thtrieu/darkflow.svg?branch=master)](https://travis-ci.org/thtrieu/darkflow) [![codecov](https://codecov.io/gh/thtrieu/darkflow/branch/master/graph/badge.svg)](https://codecov.io/gh/thtrieu/darkflow)
+
+Real-time object detection and classification. Paper: [version 1](https://arxiv.org/pdf/1506.02640.pdf), [version 2](https://arxiv.org/pdf/1612.08242.pdf).
+
+Read more about YOLO (in darknet) and download weight files [here](http://pjreddie.com/darknet/yolo/). In case the weight file cannot be found, I uploaded some of mine [here](https://drive.google.com/drive/folders/0B1tW_VtY7onidEwyQ2FtQVplWEU), which include `yolo-full` and `yolo-tiny` of v1.0, `tiny-yolo-v1.1` of v1.1 and `yolo`, `tiny-yolo-voc` of v2.
+
+
+Click on this image to see demo from yolov2:
+
+[![img](preview.png)](http://i.imgur.com/EyZZKAA.gif)
+
+## Dependencies
+
+Python3, tensorflow 1.0, numpy, opencv 3.
+
+### Getting started
+
+You can choose _one_ of the following three ways to get started with darkflow.
+
+1. Just build the Cython extensions in place. NOTE: If installing this way you will have to use `./flow` in the cloned darkflow directory instead of `flow` as darkflow is not installed globally.
+    ```
+    python3 setup.py build_ext --inplace
+    ```
+
+2. Let pip install darkflow globally in dev mode (still globally accessible, but changes to the code immediately take effect)
+    ```
+    pip install -e .
+    ```
+
+3. Install with pip globally
+    ```
+    pip install .
+    ```
+
+## Update
+
+**Android demo on Tensorflow's** [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/android/src/org/tensorflow/demo/TensorFlowYoloDetector.java)
+
+**I am looking for help:**
+ - `help wanted` labels in issue track
+
+## Parsing the annotations
+
+Skip this if you are not training or fine-tuning anything (you simply want to forward flow a trained net)
+
+For example, if you want to work with only 3 classes `tvmonitor`, `person`, `pottedplant`; edit `labels.txt` as follows
+
+```
+tvmonitor
+person
+pottedplant
+```
+
+And that's it. `darkflow` will take care of the rest. You can also set darkflow to load from a custom labels file with the `--labels` flag (i.e. `--labels myOtherLabelsFile.txt`). This can be helpful when working with multiple models with different sets of output labels. When this flag is not set, darkflow will load from `labels.txt` by default (unless you are using one of the recognized `.cfg` files designed for the COCO or VOC dataset - then the labels file will be ignored and the COCO or VOC labels will be loaded).
+
+## Design the net
+
+Skip this if you are working with one of the original configurations since they are already there. Otherwise, see the following example:
+
+```python
+...
+
+[convolutional]
+batch_normalize = 1
+size = 3
+stride = 1
+pad = 1
+activation = leaky
+
+[maxpool]
+
+[connected]
+output = 4096
+activation = linear
+
+...
+```
+
+## Flowing the graph using `flow`
+
+```bash
+# Have a look at its options
+flow --h
+```
+
+First, let's take a closer look at one of a very useful option `--load`
+
+```bash
+# 1. Load yolo-tiny.weights
+flow --model cfg/yolo-tiny.cfg --load bin/yolo-tiny.weights
+
+# 2. To completely initialize a model, leave the --load option
+flow --model cfg/yolo-new.cfg
+
+# 3. It is useful to reuse the first identical layers of tiny for `yolo-new`
+flow --model cfg/yolo-new.cfg --load bin/yolo-tiny.weights
+# this will print out which layers are reused, which are initialized
+```
+
+All input images from default folder `sample_img/` are flowed through the net and predictions are put in `sample_img/out/`. We can always specify more parameters for such forward passes, such as detection threshold, batch size, images folder, etc.
+
+```bash
+# Forward all images in sample_img/ using tiny yolo and 100% GPU usage
+flow --imgdir sample_img/ --model cfg/yolo-tiny.cfg --load bin/yolo-tiny.weights --gpu 1.0
+```
+json output can be generated with descriptions of the pixel location of each bounding box and the pixel location. Each prediction is stored in the `sample_img/out` folder by default. An example json array is shown below.
+```bash
+# Forward all images in sample_img/ using tiny yolo and JSON output.
+flow --imgdir sample_img/ --model cfg/yolo-tiny.cfg --load bin/yolo-tiny.weights --json
+```
+JSON output:
+```json
+[{"label":"person", "confidence": 0.56, "topleft": {"x": 184, "y": 101}, "bottomright": {"x": 274, "y": 382}},
+{"label": "dog", "confidence": 0.32, "topleft": {"x": 71, "y": 263}, "bottomright": {"x": 193, "y": 353}},
+{"label": "horse", "confidence": 0.76, "topleft": {"x": 412, "y": 109}, "bottomright": {"x": 592,"y": 337}}]
+```
+ - label: self explanatory
+ - confidence: somewhere between 0 and 1 (how confident yolo is about that detection)
+ - topleft: pixel coordinate of top left corner of box.
+ - bottomright: pixel coordinate of bottom right corner of box.
+
+## Training new model
+
+Training is simple as you only have to add option `--train`. Training set and annotation will be parsed if this is the first time a new configuration is trained. To point to training set and annotations, use option `--dataset` and `--annotation`. A few examples:
+
+```bash
+# Initialize yolo-new from yolo-tiny, then train the net on 100% GPU:
+flow --model cfg/yolo-new.cfg --load bin/yolo-tiny.weights --train --gpu 1.0
+
+# Completely initialize yolo-new and train it with ADAM optimizer
+flow --model cfg/yolo-new.cfg --train --trainer adam
+```
+
+During training, the script will occasionally save intermediate results into Tensorflow checkpoints, stored in `ckpt/`. To resume to any checkpoint before performing training/testing, use `--load [checkpoint_num]` option, if `checkpoint_num < 0`, `darkflow` will load the most recent save by parsing `ckpt/checkpoint`.
+
+```bash
+# Resume the most recent checkpoint for training
+flow --train --model cfg/yolo-new.cfg --load -1
+
+# Test with checkpoint at step 1500
+flow --model cfg/yolo-new.cfg --load 1500
+
+# Fine tuning yolo-tiny from the original one
+flow --train --model cfg/yolo-tiny.cfg --load bin/yolo-tiny.weights
+```
+
+Example of training on Pascal VOC 2007:
+```bash
+# Download the Pascal VOC dataset:
+curl -O https://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar
+tar xf VOCtest_06-Nov-2007.tar
+
+# An example of the Pascal VOC annotation format:
+vim VOCdevkit/VOC2007/Annotations/000001.xml
+
+# Train the net on the Pascal dataset:
+flow --model cfg/yolo-new.cfg --train --dataset "~/VOCdevkit/VOC2007/JPEGImages" --annotation "~/VOCdevkit/VOC2007/Annotations"
+```
+
+### Training on your own dataset
+
+*The steps below assume we want to use tiny YOLO and our dataset has 3 classes*
+
+1. Create a copy of the configuration file `tiny-yolo-voc.cfg` and rename it according to your preference `tiny-yolo-voc-3c.cfg` (It is crucial that you leave the original `tiny-yolo-voc.cfg` file unchanged, see below for explanation).
+
+2. In `tiny-yolo-voc-3c.cfg`, change classes in the [region] layer (the last layer) to the number of classes you are going to train for. In our case, classes are set to 3.
+    
+    ```python
+    ...
+
+    [region]
+    anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
+    bias_match=1
+    classes=3
+    coords=4
+    num=5
+    softmax=1
+    
+    ...
+    ```
+
+3. In `tiny-yolo-voc-3c.cfg`, change filters in the [convolutional] layer (the second to last layer) to num * (classes + 5). In our case, num is 5 and classes are 3 so 5 * (3 + 5) = 40 therefore filters are set to 40.
+    
+    ```python
+    ...
+
+    [convolutional]
+    size=1
+    stride=1
+    pad=1
+    filters=40
+    activation=linear
+
+    [region]
+    anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
+    
+    ...
+    ```
+
+4. Change `labels.txt` to include the label(s) you want to train on (number of labels should be the same as the number of classes you set in `tiny-yolo-voc-3c.cfg` file). In our case, `labels.txt` will contain 3 labels.
+
+    ```
+    label1
+    label2
+    label3
+    ```
+5. Reference the `tiny-yolo-voc-3c.cfg` model when you train.
+
+    `flow --model cfg/tiny-yolo-voc-3c.cfg --load bin/tiny-yolo-voc.weights --train --annotation train/Annotations --dataset train/Images`
+
+
+* Why should I leave the original `tiny-yolo-voc.cfg` file unchanged?
+    
+    When darkflow sees you are loading `tiny-yolo-voc.weights` it will look for `tiny-yolo-voc.cfg` in your cfg/ folder and compare that configuration file to the new one you have set with `--model cfg/tiny-yolo-voc-3c.cfg`. In this case, every layer will have the same exact number of weights except for the last two, so it will load the weights into all layers up to the last two because they now contain different number of weights.
+
+
+## Camera/video file demo
+
+For a demo that entirely runs on the CPU:
+
+```bash
+flow --model cfg/yolo-new.cfg --load bin/yolo-new.weights --demo videofile.avi
+```
+
+For a demo that runs 100% on the GPU:
+
+```bash
+flow --model cfg/yolo-new.cfg --load bin/yolo-new.weights --demo videofile.avi --gpu 1.0
+```
+
+To use your webcam/camera, simply replace `videofile.avi` with keyword `camera`.
+
+To save a video with predicted bounding box, add `--saveVideo` option.
+
+## Using darkflow from another python application
+
+Please note that `return_predict(img)` must take an `numpy.ndarray`. Your image must be loaded beforehand and passed to `return_predict(img)`. Passing the file path won't work.
+
+Result from `return_predict(img)` will be a list of dictionaries representing each detected object's values in the same format as the JSON output listed above.
+
+```python
+from darkflow.net.build import TFNet
+import cv2
+
+options = {"model": "cfg/yolo.cfg", "load": "bin/yolo.weights", "threshold": 0.1}
+
+tfnet = TFNet(options)
+
+imgcv = cv2.imread("./sample_img/dog.jpg")
+result = tfnet.return_predict(imgcv)
+print(result)
+```
+
+
+## Save the built graph to a protobuf file (`.pb`)
+
+```bash
+## Saving the lastest checkpoint to protobuf file
+flow --model cfg/yolo-new.cfg --load -1 --savepb
+
+## Saving graph and weights to protobuf file
+flow --model cfg/yolo.cfg --load bin/yolo.weights --savepb
+```
+When saving the `.pb` file, a `.meta` file will also be generated alongside it. This `.meta` file is a JSON dump of everything in the `meta` dictionary that contains information nessecary for post-processing such as `anchors` and `labels`. This way, everything you need to make predictions from the graph and do post processing is contained in those two files - no need to have the `.cfg` or any labels file tagging along.
+
+The created `.pb` file can be used to migrate the graph to mobile devices (JAVA / C++ / Objective-C++). The name of input tensor and output tensor are respectively `'input'` and `'output'`. For further usage of this protobuf file, please refer to the official documentation of `Tensorflow` on C++ API [_here_](https://www.tensorflow.org/versions/r0.9/api_docs/cc/index.html). To run it on, say, iOS application, simply add the file to Bundle Resources and update the path to this file inside source code.
+
+Also, darkflow supports loading from a `.pb` and `.meta` file for generating predictions (instead of loading from a `.cfg` and checkpoint or `.weights`).
+```bash
+## Forward images in sample_img for predictions based on protobuf file
+flow --pbLoad built_graph/yolo.pb --metaLoad built_graph/yolo.meta --imgdir sample_img/
+```
+If you'd like to load a `.pb` and `.meta` file when using `return_predict()` you can set the `"pbLoad"` and `"metaLoad"` options in place of the `"model"` and `"load"` options you would normally set.
+
+That's all.
diff --git a/cfg/coco.names b/cfg/coco.names
new file mode 100644
index 0000000..ca76c80
--- /dev/null
+++ b/cfg/coco.names
@@ -0,0 +1,80 @@
+person
+bicycle
+car
+motorbike
+aeroplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+sofa
+pottedplant
+bed
+diningtable
+toilet
+tvmonitor
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush
diff --git a/cfg/extraction.cfg b/cfg/extraction.cfg
new file mode 100644
index 0000000..94e1067
--- /dev/null
+++ b/cfg/extraction.cfg
@@ -0,0 +1,206 @@
+[net]
+batch=128
+subdivisions=1
+height=224
+width=224
+max_crop=320
+channels=3
+momentum=0.9
+decay=0.0005
+
+learning_rate=0.1
+policy=poly
+power=4
+max_batches=1600000
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=7
+stride=2
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=192
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1000
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[avgpool]
+
+[softmax]
+groups=1
+
+[cost]
+type=sse
+
diff --git a/cfg/extraction.conv.cfg b/cfg/extraction.conv.cfg
new file mode 100644
index 0000000..2a7d09e
--- /dev/null
+++ b/cfg/extraction.conv.cfg
@@ -0,0 +1,179 @@
+[net]
+batch=1
+subdivisions=1
+height=256
+width=256
+channels=3
+momentum=0.9
+decay=0.0005
+
+learning_rate=0.5
+policy=poly
+power=6
+max_batches=500000
+
+[convolutional]
+filters=64
+size=7
+stride=2
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=192
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[avgpool]
+
+[connected]
+output=1000
+activation=leaky
+
+[softmax]
+groups=1
+
diff --git a/cfg/tiny-yolo-4c.cfg b/cfg/tiny-yolo-4c.cfg
new file mode 100644
index 0000000..881d9bf
--- /dev/null
+++ b/cfg/tiny-yolo-4c.cfg
@@ -0,0 +1,134 @@
+[net]
+batch=64
+subdivisions=8
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+max_batches = 40100
+policy=steps
+steps=-1,100,20000,30000
+scales=.1,10,.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=1
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+###########
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=45
+activation=linear
+
+[region]
+anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
+bias_match=1
+classes=4
+coords=4
+num=5
+softmax=1
+jitter=.2
+rescore=1
+
+object_scale=5
+noobject_scale=1
+class_scale=1
+coord_scale=1
+
+absolute=1
+thresh=.6
+random=1
diff --git a/cfg/tiny-yolo-voc.cfg b/cfg/tiny-yolo-voc.cfg
new file mode 100644
index 0000000..032b747
--- /dev/null
+++ b/cfg/tiny-yolo-voc.cfg
@@ -0,0 +1,134 @@
+[net]
+batch=64
+subdivisions=8
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+max_batches = 40100
+policy=steps
+steps=-1,100,20000,30000
+scales=.1,10,.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=1
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+###########
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=125
+activation=linear
+
+[region]
+anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
+bias_match=1
+classes=20
+coords=4
+num=5
+softmax=1
+jitter=.2
+rescore=1
+
+object_scale=5
+noobject_scale=1
+class_scale=1
+coord_scale=1
+
+absolute=1
+thresh = .5
+random=1
diff --git a/cfg/tiny-yolo.cfg b/cfg/tiny-yolo.cfg
new file mode 100644
index 0000000..5580098
--- /dev/null
+++ b/cfg/tiny-yolo.cfg
@@ -0,0 +1,134 @@
+[net]
+batch=64
+subdivisions=8
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+max_batches = 120000
+policy=steps
+steps=-1,100,80000,100000
+scales=.1,10,.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=1
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+###########
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=425
+activation=linear
+
+[region]
+anchors = 0.738768,0.874946,  2.42204,2.65704,  4.30971,7.04493,  10.246,4.59428,  12.6868,11.8741
+bias_match=1
+classes=80
+coords=4
+num=5
+softmax=1
+jitter=.2
+rescore=1
+
+object_scale=5
+noobject_scale=1
+class_scale=1
+coord_scale=1
+
+absolute=1
+thresh = .6
+random=1
diff --git a/cfg/v1.1/person-bottle.cfg b/cfg/v1.1/person-bottle.cfg
new file mode 100644
index 0000000..e5c0a25
--- /dev/null
+++ b/cfg/v1.1/person-bottle.cfg
@@ -0,0 +1,128 @@
+[net]
+batch=64
+subdivisions=2
+height=448
+width=448
+channels=3
+momentum=0.9
+decay=0.0005
+
+saturation=.75
+exposure=.75
+hue = .1
+
+learning_rate=0.0005
+policy=steps
+steps=200,400,600,800,20000,30000
+scales=2.5,2,2,2,.1,.1
+max_batches = 40000
+
+[convolutional]
+batch_normalize=1
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[select]
+old_output=1470
+keep=4,14/20
+bins=49
+output=588
+activation=linear
+
+[detection]
+classes=2
+coords=4
+rescore=1
+side=7
+num=2
+softmax=0
+sqrt=1
+jitter=.2
+
+object_scale=1
+noobject_scale=.5
+class_scale=1
+coord_scale=5
\ No newline at end of file
diff --git a/cfg/v1.1/tiny-coco.cfg b/cfg/v1.1/tiny-coco.cfg
new file mode 100644
index 0000000..e58c73a
--- /dev/null
+++ b/cfg/v1.1/tiny-coco.cfg
@@ -0,0 +1,125 @@
+[net]
+batch=64
+subdivisions=2
+height=448
+width=448
+channels=3
+momentum=0.9
+decay=0.0005
+
+hue = .1
+saturation=.75
+exposure=.75
+
+learning_rate=0.0005
+policy=steps
+steps=200,400,600,800,100000,150000
+scales=2.5,2,2,2,.1,.1
+max_batches = 200000
+
+[convolutional]
+batch_normalize=1
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[connected]
+output= 4655
+activation=linear
+
+[detection]
+classes=80
+coords=4
+rescore=1
+side=7
+num=3
+softmax=0
+sqrt=1
+jitter=.2
+
+object_scale=1
+noobject_scale=.5
+class_scale=1
+coord_scale=5
diff --git a/cfg/v1.1/tiny-yolo-4c.cfg b/cfg/v1.1/tiny-yolo-4c.cfg
new file mode 100644
index 0000000..22d862e
--- /dev/null
+++ b/cfg/v1.1/tiny-yolo-4c.cfg
@@ -0,0 +1,128 @@
+[net]
+batch=64
+subdivisions=2
+height=448
+width=448
+channels=3
+momentum=0.9
+decay=0.0005
+
+saturation=.75
+exposure=.75
+hue = .1
+
+learning_rate=0.0005
+policy=steps
+steps=200,400,600,800,20000,30000
+scales=2.5,2,2,2,.1,.1
+max_batches = 40000
+
+[convolutional]
+batch_normalize=1
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[select]
+old_output=1470
+keep=8,14,15,19/20
+bins=49
+output=686
+activation=linear
+
+[detection]
+classes=4
+coords=4
+rescore=1
+side=7
+num=2
+softmax=0
+sqrt=1
+jitter=.2
+
+object_scale=1
+noobject_scale=.5
+class_scale=1
+coord_scale=5
\ No newline at end of file
diff --git a/cfg/v1.1/tiny-yolov1.cfg b/cfg/v1.1/tiny-yolov1.cfg
new file mode 100644
index 0000000..ac4b346
--- /dev/null
+++ b/cfg/v1.1/tiny-yolov1.cfg
@@ -0,0 +1,126 @@
+[net]
+batch=64
+subdivisions=2
+height=448
+width=448
+channels=3
+momentum=0.9
+decay=0.0005
+
+saturation=.75
+exposure=.75
+hue = .1
+
+learning_rate=0.0005
+policy=steps
+steps=200,400,600,800,20000,30000
+scales=2.5,2,2,2,.1,.1
+max_batches = 40000
+
+[convolutional]
+batch_normalize=1
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[connected]
+output= 1470
+activation=linear
+
+[detection]
+classes=20
+coords=4
+rescore=1
+side=7
+num=2
+softmax=0
+sqrt=1
+jitter=.2
+
+object_scale=1
+noobject_scale=.5
+class_scale=1
+coord_scale=5
+
diff --git a/cfg/v1.1/yolo-coco.cfg b/cfg/v1.1/yolo-coco.cfg
new file mode 100644
index 0000000..ed3f2d6
--- /dev/null
+++ b/cfg/v1.1/yolo-coco.cfg
@@ -0,0 +1,255 @@
+[net]
+batch=64
+subdivisions=4
+height=448
+width=448
+channels=3
+momentum=0.9
+decay=0.0005
+
+hue = .1
+saturation=.75
+exposure=.75
+
+learning_rate=0.0005
+policy=steps
+steps=200,400,600,800,100000,150000
+scales=2.5,2,2,2,.1,.1
+max_batches = 200000
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=7
+stride=2
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=192
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+#######
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=2
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[local]
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[connected]
+output= 4655
+activation=linear
+
+[detection]
+classes=80
+coords=4
+rescore=1
+side=7
+num=3
+softmax=0
+sqrt=1
+jitter=.2
+
+object_scale=1
+noobject_scale=.5
+class_scale=1
+coord_scale=5
+
diff --git a/cfg/v1.1/yolov1.cfg b/cfg/v1.1/yolov1.cfg
new file mode 100644
index 0000000..c4f415c
--- /dev/null
+++ b/cfg/v1.1/yolov1.cfg
@@ -0,0 +1,257 @@
+[net]
+batch=1
+subdivisions=1
+height=448
+width=448
+channels=3
+momentum=0.9
+decay=0.0005
+saturation=1.5
+exposure=1.5
+hue=.1
+
+learning_rate=0.0005
+policy=steps
+steps=200,400,600,20000,30000
+scales=2.5,2,2,.1,.1
+max_batches = 40000
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=7
+stride=2
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=192
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+#######
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=2
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[local]
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[dropout]
+probability=.5
+
+[connected]
+output= 1715
+activation=linear
+
+[detection]
+classes=20
+coords=4
+rescore=1
+side=7
+num=3
+softmax=0
+sqrt=1
+jitter=.2
+
+object_scale=1
+noobject_scale=.5
+class_scale=1
+coord_scale=5
+
diff --git a/cfg/v1/tiny-old.profile b/cfg/v1/tiny-old.profile
new file mode 100644
index 0000000..0799061
Binary files /dev/null and b/cfg/v1/tiny-old.profile differ
diff --git a/cfg/v1/tiny.profile b/cfg/v1/tiny.profile
new file mode 100644
index 0000000..e6635ae
--- /dev/null
+++ b/cfg/v1/tiny.profile
@@ -0,0 +1 @@
+€]q
]qa.
\ No newline at end of file
diff --git a/cfg/v1/yolo-2c.cfg b/cfg/v1/yolo-2c.cfg
new file mode 100644
index 0000000..bde5bce
--- /dev/null
+++ b/cfg/v1/yolo-2c.cfg
@@ -0,0 +1,141 @@
+[net]
+batch=64
+subdivisions=64
+height=448
+width=448
+channels=3
+momentum=0.9
+decay=0.0005
+
+learning_rate=0.0001
+policy=steps
+steps=20,40,60,80,20000,30000
+scales=5,5,2,2,.1,.1
+max_batches = 40000
+
+[crop]
+crop_width=448
+crop_height=448
+flip=0
+angle=0
+saturation = 1.5
+exposure = 1.5
+
+[convolutional]
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[connected]
+output=256
+activation=linear
+
+[connected]
+output=4096
+activation=leaky
+
+[dropout]
+probability=.5
+
+[select]
+old_output=1470
+keep=14,19/20
+bins=49
+output=588
+activation=linear
+
+[detection]
+classes=2
+coords=4
+rescore=1
+side=7
+num=2
+softmax=0
+sqrt=1
+jitter=.2
+object_scale=1
+noobject_scale=.5
+class_scale=1
+coord_scale=5
\ No newline at end of file
diff --git a/cfg/v1/yolo-4c.cfg b/cfg/v1/yolo-4c.cfg
new file mode 100644
index 0000000..ecf46d2
--- /dev/null
+++ b/cfg/v1/yolo-4c.cfg
@@ -0,0 +1,237 @@
+[net]
+batch=64
+subdivisions=64
+height=448
+width=448
+channels=3
+momentum=0.9
+decay=0.0005
+
+learning_rate=0.001
+policy=steps
+steps=200,400,600,20000,30000
+scales=2.5,2,2,.1,.1
+max_batches = 40000
+
+[crop]
+crop_width=448
+crop_height=448
+flip=0
+angle=0
+saturation = 1.5
+exposure = 1.5
+
+[convolutional]
+filters=64
+size=7
+stride=2
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=192
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+#######
+
+[convolutional]
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=3
+stride=2
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[connected]
+output=4096
+activation=leaky
+
+[dropout]
+probability=.5
+
+[select]
+old_output=1470
+keep=8,14,15,19/20
+bins=49
+output=686
+activation=linear
+
+[detection]
+classes=4
+coords=4
+rescore=1
+side=7
+num=2
+softmax=0
+sqrt=1
+jitter=.2
+
+object_scale=1
+noobject_scale=.5
+class_scale=1
+coord_scale=5
\ No newline at end of file
diff --git a/cfg/v1/yolo-full.cfg b/cfg/v1/yolo-full.cfg
new file mode 100644
index 0000000..9eb08d9
--- /dev/null
+++ b/cfg/v1/yolo-full.cfg
@@ -0,0 +1,234 @@
+[net]
+batch=64
+subdivisions=64
+height=448
+width=448
+channels=3
+momentum=0.9
+decay=0.0005
+
+learning_rate=0.001
+policy=steps
+steps=200,400,600,20000,30000
+scales=2.5,2,2,.1,.1
+max_batches = 40000
+
+[crop]
+crop_width=448
+crop_height=448
+flip=0
+angle=0
+saturation = 1.5
+exposure = 1.5
+
+[convolutional]
+filters=64
+size=7
+stride=2
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=192
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+#######
+
+[convolutional]
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=3
+stride=2
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[connected]
+output=4096
+activation=leaky
+
+[dropout]
+probability=.5
+
+[connected]
+output= 1470
+activation=linear
+
+[detection]
+classes=20
+coords=4
+rescore=1
+side=7
+num=2
+softmax=0
+sqrt=1
+jitter=.2
+
+object_scale=1
+noobject_scale=.5
+class_scale=1
+coord_scale=5
\ No newline at end of file
diff --git a/cfg/v1/yolo-small.cfg b/cfg/v1/yolo-small.cfg
new file mode 100644
index 0000000..2a84485
--- /dev/null
+++ b/cfg/v1/yolo-small.cfg
@@ -0,0 +1,239 @@
+[net]
+batch=64
+subdivisions=64
+height=448
+width=448
+channels=3
+momentum=0.9
+decay=0.0005
+
+learning_rate=0.001
+policy=steps
+steps=200,400,600,20000,30000
+scales=2.5,2,2,.1,.1
+max_batches = 40000
+
+[crop]
+crop_width=448
+crop_height=448
+flip=0
+angle=0
+saturation = 1.5
+exposure = 1.5
+
+[convolutional]
+filters=64
+size=7
+stride=2
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=192
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+#######
+
+[convolutional]
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=3
+stride=2
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[connected]
+output=512
+activation=leaky
+
+[connected]
+output=4096
+activation=leaky
+
+[dropout]
+probability=.5
+
+[connected]
+output= 1470
+activation=linear
+
+[detection]
+classes=20
+coords=4
+rescore=1
+side=7
+num=2
+softmax=0
+sqrt=1
+jitter=.2
+
+object_scale=1
+noobject_scale=.5
+class_scale=1
+coord_scale=5
+
diff --git a/cfg/v1/yolo-tiny-extract.cfg b/cfg/v1/yolo-tiny-extract.cfg
new file mode 100644
index 0000000..cddf222
--- /dev/null
+++ b/cfg/v1/yolo-tiny-extract.cfg
@@ -0,0 +1,175 @@
+[net]
+batch=64
+subdivisions=64
+height=448
+width=448
+channels=3
+momentum=0.9
+decay=0.0005
+
+learning_rate=0.0001
+policy=steps
+steps=20,40,60,80,20000,30000
+scales=5,5,2,2,.1,.1
+max_batches = 40000
+
+[crop]
+crop_width=448
+crop_height=448
+flip=0
+angle=0
+saturation = 1.5
+exposure = 1.5
+
+[conv-extract]
+profile=cfg/v1/tiny.profile
+input=-1
+output=0
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[conv-extract]
+profile=cfg/v1/tiny.profile
+input=0
+output=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[conv-extract]
+profile=cfg/v1/tiny.profile
+input=1
+output=2
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[conv-extract]
+profile=cfg/v1/tiny.profile
+input=2
+output=3
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[conv-extract]
+profile=cfg/v1/tiny.profile
+input=3
+output=4
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[conv-extract]
+profile=cfg/v1/tiny.profile
+input=4
+output=5
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[conv-extract]
+profile=cfg/v1/tiny.profile
+input=5
+output=6
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[conv-extract]
+profile=cfg/v1/tiny.profile
+input=6
+output=7
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[conv-extract]
+profile=cfg/v1/tiny.profile
+input=7
+output=8
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[extract]
+profile=cfg/v1/tiny.profile
+input=8
+output=9
+old=7,7,1024,256
+activation=linear
+
+[extract]
+profile=cfg/v1/tiny.profile
+input=9
+output=10
+old=256,4096
+activation=leaky
+
+[dropout]
+probability=1.
+
+[select]
+input=cfg/v1/tiny.profile,10
+old_output=1470
+keep=8,14,15,19/20
+bins=49
+output=686
+activation=linear
+
+[detection]
+classes=4
+coords=4
+rescore=1
+side=7
+num=2
+softmax=0
+sqrt=1
+jitter=.2
+object_scale=1
+noobject_scale=.5
+class_scale=1
+coord_scale=5
\ No newline at end of file
diff --git a/cfg/v1/yolo-tiny-extract_.cfg b/cfg/v1/yolo-tiny-extract_.cfg
new file mode 100644
index 0000000..21e250e
--- /dev/null
+++ b/cfg/v1/yolo-tiny-extract_.cfg
@@ -0,0 +1,177 @@
+[net]
+batch=64
+subdivisions=64
+height=448
+width=448
+channels=3
+momentum=0.9
+decay=0.0005
+
+learning_rate=0.0001
+policy=steps
+steps=20,40,60,80,20000,30000
+scales=5,5,2,2,.1,.1
+max_batches = 40000
+
+[crop]
+crop_width=448
+crop_height=448
+flip=0
+angle=0
+saturation = 1.5
+exposure = 1.5
+
+[conv-extract]
+profile=cfg/v1/tiny-old.profile
+input=-1
+output=0
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[conv-extract]
+profile=cfg/v1/tiny-old.profile
+input=0
+output=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[conv-extract]
+profile=cfg/v1/tiny-old.profile
+input=1
+output=2
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[conv-extract]
+profile=cfg/v1/tiny-old.profile
+input=2
+output=3
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[conv-extract]
+profile=cfg/v1/tiny-old.profile
+input=3
+output=4
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[conv-extract]
+profile=cfg/v1/tiny-old.profile
+input=4
+output=5
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[conv-extract]
+profile=cfg/v1/tiny-old.profile
+input=5
+output=6
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[conv-extract]
+profile=cfg/v1/tiny-old.profile
+input=6
+output=7
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[conv-extract]
+profile=cfg/v1/tiny-old.profile
+input=7
+output=8
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[extract]
+profile=cfg/v1/tiny-old.profile
+input=8
+output=9
+old=7,7,1024,256
+activation=linear
+
+[extract]
+profile=cfg/v1/tiny-old.profile
+input=9
+output=10
+old=256,4096
+activation=leaky
+
+[dropout]
+probability=1.
+
+[select]
+input=cfg/v1/tiny-old.profile,10
+old_output=1470
+keep=8,14,15,19/20
+bins=49
+output=686
+activation=linear
+
+[detection]
+classes=4
+coords=4
+rescore=1
+side=7
+num=2
+softmax=0
+sqrt=1
+jitter=.2
+object_scale=2.5
+noobject_scale=2
+class_scale=2.5
+coord_scale=5
+
+save=11250
\ No newline at end of file
diff --git a/cfg/v1/yolo-tiny.cfg b/cfg/v1/yolo-tiny.cfg
new file mode 100644
index 0000000..8d139e8
--- /dev/null
+++ b/cfg/v1/yolo-tiny.cfg
@@ -0,0 +1,138 @@
+[net]
+batch=64
+subdivisions=64
+height=448
+width=448
+channels=3
+momentum=0.9
+decay=0.0005
+
+learning_rate=0.0001
+policy=steps
+steps=20,40,60,80,20000,30000
+scales=5,5,2,2,.1,.1
+max_batches = 40000
+
+[crop]
+crop_width=448
+crop_height=448
+flip=0
+angle=0
+saturation = 1.5
+exposure = 1.5
+
+[convolutional]
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[connected]
+output=256
+activation=linear
+
+[connected]
+output=4096
+activation=leaky
+
+[dropout]
+probability=.5
+
+[connected]
+output= 1470
+activation=linear
+
+[detection]
+classes=20
+coords=4
+rescore=1
+side=7
+num=2
+softmax=0
+sqrt=1
+jitter=.2
+object_scale=1
+noobject_scale=.5
+class_scale=1
+coord_scale=5
\ No newline at end of file
diff --git a/cfg/v1/yolo-tiny4c.cfg b/cfg/v1/yolo-tiny4c.cfg
new file mode 100644
index 0000000..21357ac
--- /dev/null
+++ b/cfg/v1/yolo-tiny4c.cfg
@@ -0,0 +1,141 @@
+[net]
+batch=64
+subdivisions=64
+height=448
+width=448
+channels=3
+momentum=0.9
+decay=0.0005
+
+learning_rate=0.0001
+policy=steps
+steps=20,40,60,80,20000,30000
+scales=5,5,2,2,.1,.1
+max_batches = 40000
+
+[crop]
+crop_width=448
+crop_height=448
+flip=0
+angle=0
+saturation = 1.5
+exposure = 1.5
+
+[convolutional]
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[connected]
+output=256
+activation=linear
+
+[connected]
+output=4096
+activation=leaky
+
+[dropout]
+probability=.5
+
+[select]
+old_output=1470
+keep=8,14,15,19/20
+bins=49
+output=686
+activation=linear
+
+[detection]
+classes=4
+coords=4
+rescore=1
+side=7
+num=2
+softmax=0
+sqrt=1
+jitter=.2
+object_scale=1
+noobject_scale=.5
+class_scale=1
+coord_scale=5
\ No newline at end of file
diff --git a/cfg/yolo-voc.cfg b/cfg/yolo-voc.cfg
new file mode 100644
index 0000000..ceb3f2a
--- /dev/null
+++ b/cfg/yolo-voc.cfg
@@ -0,0 +1,244 @@
+[net]
+batch=64
+subdivisions=8
+height=416
+width=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.0001
+max_batches = 45000
+policy=steps
+steps=100,25000,35000
+scales=10,.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+
+#######
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[route]
+layers=-9
+
+[reorg]
+stride=2
+
+[route]
+layers=-1,-3
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=125
+activation=linear
+
+[region]
+anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
+bias_match=1
+classes=20
+coords=4
+num=5
+softmax=1
+jitter=.2
+rescore=1
+
+object_scale=5
+noobject_scale=1
+class_scale=1
+coord_scale=1
+
+absolute=1
+thresh = .6
+random=0
diff --git a/cfg/yolo.cfg b/cfg/yolo.cfg
new file mode 100644
index 0000000..343bb25
--- /dev/null
+++ b/cfg/yolo.cfg
@@ -0,0 +1,258 @@
+[net]
+# Testing
+batch=1
+subdivisions=1
+# Training
+# batch=64
+# subdivisions=8
+width=608
+height=608
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+
+#######
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[route]
+layers=-9
+
+[convolutional]
+batch_normalize=1
+size=1
+stride=1
+pad=1
+filters=64
+activation=leaky
+
+[reorg]
+stride=2
+
+[route]
+layers=-1,-4
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=425
+activation=linear
+
+
+[region]
+anchors =  0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828
+bias_match=1
+classes=80
+coords=4
+num=5
+softmax=1
+jitter=.3
+rescore=1
+
+object_scale=5
+noobject_scale=1
+class_scale=1
+coord_scale=1
+
+absolute=1
+thresh = .1
+random=1
diff --git a/darkflow/cli.py b/darkflow/cli.py
new file mode 100644
index 0000000..6d13d0a
--- /dev/null
+++ b/darkflow/cli.py
@@ -0,0 +1,37 @@
+from .defaults import argHandler #Import the default arguments
+import os
+from .net.build import TFNet
+
+def cliHandler(args):
+    FLAGS = argHandler()
+    FLAGS.setDefaults()
+    FLAGS.parseArgs(args)
+
+    # make sure all necessary dirs exist
+    def _get_dir(dirs):
+        for d in dirs:
+            this = os.path.abspath(os.path.join(os.path.curdir, d))
+            if not os.path.exists(this): os.makedirs(this)
+    _get_dir([FLAGS.imgdir, FLAGS.binary, FLAGS.backup, 
+             os.path.join(FLAGS.imgdir,'out'), FLAGS.summary])
+
+    # fix FLAGS.load to appropriate type
+    try: FLAGS.load = int(FLAGS.load)
+    except: pass
+
+    tfnet = TFNet(FLAGS)
+    
+    if FLAGS.demo:
+        tfnet.camera()
+        exit('Demo stopped, exit.')
+
+    if FLAGS.train:
+        print('Enter training ...'); tfnet.train()
+        if not FLAGS.savepb: 
+            exit('Training finished, exit.')
+
+    if FLAGS.savepb:
+        print('Rebuild a constant version ...')
+        tfnet.savepb(); exit('Done')
+
+    tfnet.predict()
diff --git a/darkflow/cython_utils/cy_yolo2_findboxes.pyx b/darkflow/cython_utils/cy_yolo2_findboxes.pyx
new file mode 100644
index 0000000..299add0
--- /dev/null
+++ b/darkflow/cython_utils/cy_yolo2_findboxes.pyx
@@ -0,0 +1,97 @@
+import numpy as np
+cimport numpy as np
+cimport cython
+ctypedef np.float_t DTYPE_t
+from libc.math cimport exp
+from ..utils.box import BoundBox
+from nms cimport NMS
+
+#expit
+@cython.boundscheck(False) # turn off bounds-checking for entire function
+@cython.wraparound(False)  # turn off negative index wrapping for entire function
+@cython.cdivision(True)
+cdef float expit_c(float x):
+    cdef float y= 1/(1+exp(-x))
+    return y
+
+#MAX
+@cython.boundscheck(False) # turn off bounds-checking for entire function
+@cython.wraparound(False)  # turn off negative index wrapping for entire function
+@cython.cdivision(True)
+cdef float max_c(float a, float b):
+    if(a>b):
+        return a
+    return b
+
+"""
+#SOFTMAX!
+@cython.cdivision(True)
+@cython.boundscheck(False) # turn off bounds-checking for entire function
+@cython.wraparound(False)  # turn off negative index wrapping for entire function
+cdef void _softmax_c(float* x, int classes):
+    cdef:
+        float sum = 0
+        np.intp_t k
+        float arr_max = 0
+    for k in range(classes):
+        arr_max = max(arr_max,x[k])
+    
+    for k in range(classes):
+        x[k] = exp(x[k]-arr_max)
+        sum += x[k]
+
+    for k in range(classes):
+        x[k] = x[k]/sum
+"""
+        
+        
+
+#BOX CONSTRUCTOR
+@cython.cdivision(True)
+@cython.boundscheck(False) # turn off bounds-checking for entire function
+@cython.wraparound(False)  # turn off negative index wrapping for entire function
+def box_constructor(meta,np.ndarray[float,ndim=3] net_out_in):
+    cdef:
+        np.intp_t H, W, _, C, B, row, col, box_loop, class_loop
+        np.intp_t row1, col1, box_loop1,index,index2
+        float  threshold = meta['thresh']
+        float tempc,arr_max=0,sum=0
+        double[:] anchors = np.asarray(meta['anchors'])
+        list boxes = list()
+
+    H, W, _ = meta['out_size']
+    C = meta['classes']
+    B = meta['num']
+    
+    cdef:
+        float[:, :, :, ::1] net_out = net_out_in.reshape([H, W, B, net_out_in.shape[2]/B])
+        float[:, :, :, ::1] Classes = net_out[:, :, :, 5:]
+        float[:, :, :, ::1] Bbox_pred =  net_out[:, :, :, :5]
+        float[:, :, :, ::1] probs = np.zeros((H, W, B, C), dtype=np.float32)
+    
+    for row in range(H):
+        for col in range(W):
+            for box_loop in range(B):
+                arr_max=0
+                sum=0;
+                Bbox_pred[row, col, box_loop, 4] = expit_c(Bbox_pred[row, col, box_loop, 4])
+                Bbox_pred[row, col, box_loop, 0] = (col + expit_c(Bbox_pred[row, col, box_loop, 0])) / W
+                Bbox_pred[row, col, box_loop, 1] = (row + expit_c(Bbox_pred[row, col, box_loop, 1])) / H
+                Bbox_pred[row, col, box_loop, 2] = exp(Bbox_pred[row, col, box_loop, 2]) * anchors[2 * box_loop + 0] / W
+                Bbox_pred[row, col, box_loop, 3] = exp(Bbox_pred[row, col, box_loop, 3]) * anchors[2 * box_loop + 1] / H
+                #SOFTMAX BLOCK, no more pointer juggling
+                for class_loop in range(C):
+                    arr_max=max_c(arr_max,Classes[row,col,box_loop,class_loop])
+                
+                for class_loop in range(C):
+                    Classes[row,col,box_loop,class_loop]=exp(Classes[row,col,box_loop,class_loop]-arr_max)
+                    sum+=Classes[row,col,box_loop,class_loop]
+                
+                for class_loop in range(C):
+                    tempc = Classes[row, col, box_loop, class_loop] * Bbox_pred[row, col, box_loop, 4]/sum                    
+                    if(tempc > threshold):
+                        probs[row, col, box_loop, class_loop] = tempc
+    
+    
+    #NMS                    
+    return NMS(np.ascontiguousarray(probs).reshape(H*W*B,C), np.ascontiguousarray(Bbox_pred).reshape(H*B*W,5))
diff --git a/darkflow/cython_utils/cy_yolo_findboxes.pyx b/darkflow/cython_utils/cy_yolo_findboxes.pyx
new file mode 100644
index 0000000..1f65408
--- /dev/null
+++ b/darkflow/cython_utils/cy_yolo_findboxes.pyx
@@ -0,0 +1,51 @@
+import numpy as np
+cimport numpy as np
+cimport cython
+ctypedef np.float_t DTYPE_t
+from libc.math cimport exp
+from ..utils.box import BoundBox
+from nms cimport NMS
+
+
+
+@cython.cdivision(True)
+@cython.boundscheck(False) # turn off bounds-checking for entire function
+@cython.wraparound(False)  # turn off negative index wrapping for entire function
+def yolo_box_constructor(meta,np.ndarray[float] net_out, float threshold):
+
+    cdef:
+        float sqrt
+        int C,B,S
+        int SS,prob_size,conf_size
+        int grid, b
+        int class_loop
+
+    
+    sqrt =  meta['sqrt'] + 1
+    C, B, S = meta['classes'], meta['num'], meta['side']
+    boxes = []
+    SS        =  S * S # number of grid cells
+    prob_size = SS * C # class probabilities
+    conf_size = SS * B # confidences for each grid cell
+
+    cdef:
+        float [:,::1] probs =  np.ascontiguousarray(net_out[0 : prob_size]).reshape([SS,C])
+        float [:,::1] confs =  np.ascontiguousarray(net_out[prob_size : (prob_size + conf_size)]).reshape([SS,B])
+        float [: , : ,::1] coords =  np.ascontiguousarray(net_out[(prob_size + conf_size) : ]).reshape([SS, B, 4])
+        float [:,:,::1] final_probs = np.zeros([SS,B,C],dtype=np.float32)
+        
+    
+    for grid in range(SS):
+        for b in range(B):
+            coords[grid, b, 0] = (coords[grid, b, 0] + grid %  S) / S
+            coords[grid, b, 1] = (coords[grid, b, 1] + grid // S) / S
+            coords[grid, b, 2] =  coords[grid, b, 2] ** sqrt
+            coords[grid, b, 3] =  coords[grid, b, 3] ** sqrt
+            for class_loop in range(C):
+                probs[grid, class_loop] = probs[grid, class_loop] * confs[grid, b]
+                #print("PROBS",probs[grid,class_loop])
+                if(probs[grid,class_loop] > threshold ):
+                    final_probs[grid, b, class_loop] = probs[grid, class_loop]
+    
+    
+    return NMS(np.ascontiguousarray(final_probs).reshape(SS*B, C) , np.ascontiguousarray(coords).reshape(SS*B, 4))
diff --git a/darkflow/cython_utils/nms.pxd b/darkflow/cython_utils/nms.pxd
new file mode 100644
index 0000000..018365e
--- /dev/null
+++ b/darkflow/cython_utils/nms.pxd
@@ -0,0 +1,11 @@
+import numpy as np
+cimport numpy as np
+cimport cython
+ctypedef np.float_t DTYPE_t
+from libc.math cimport exp
+from utils.box import BoundBox
+
+
+cdef NMS(float[:, ::1] , float[:, ::1] )
+
+
diff --git a/darkflow/cython_utils/nms.pyx b/darkflow/cython_utils/nms.pyx
new file mode 100644
index 0000000..cf264d6
--- /dev/null
+++ b/darkflow/cython_utils/nms.pyx
@@ -0,0 +1,131 @@
+import numpy as np
+cimport numpy as np
+cimport cython
+from libc.math cimport exp
+from ..utils.box import BoundBox
+
+
+
+#OVERLAP
+@cython.boundscheck(False) # turn off bounds-checking for entire function
+@cython.wraparound(False)  # turn off negative index wrapping for entire function
+@cython.cdivision(True)
+cdef float overlap_c(float x1, float w1 , float x2 , float w2):
+    cdef:
+        float l1,l2,left,right
+    l1 = x1 - w1 /2.
+    l2 = x2 - w2 /2.
+    left = max(l1,l2)
+    r1 = x1 + w1 /2.
+    r2 = x2 + w2 /2.
+    right = min(r1, r2)
+    return right - left;
+
+#BOX INTERSECTION
+@cython.boundscheck(False) # turn off bounds-checking for entire function
+@cython.wraparound(False)  # turn off negative index wrapping for entire function
+@cython.cdivision(True)
+cdef float box_intersection_c(float ax, float ay, float aw, float ah, float bx, float by, float bw, float bh):
+    cdef:
+        float w,h,area
+    w = overlap_c(ax, aw, bx, bw)
+    h = overlap_c(ay, ah, by, bh)
+    if w < 0 or h < 0: return 0
+    area = w * h
+    return area
+
+#BOX UNION
+@cython.boundscheck(False) # turn off bounds-checking for entire function
+@cython.wraparound(False)  # turn off negative index wrapping for entire function
+@cython.cdivision(True)
+cdef float box_union_c(float ax, float ay, float aw, float ah, float bx, float by, float bw, float bh):
+    cdef:
+        float i,u
+    i = box_intersection_c(ax, ay, aw, ah, bx, by, bw, bh)
+    u = aw * ah + bw * bh -i
+    return u
+
+
+#BOX IOU
+@cython.boundscheck(False) # turn off bounds-checking for entire function
+@cython.wraparound(False)  # turn off negative index wrapping for entire function
+@cython.cdivision(True)
+cdef float box_iou_c(float ax, float ay, float aw, float ah, float bx, float by, float bw, float bh):
+    return box_intersection_c(ax, ay, aw, ah, bx, by, bw, bh) / box_union_c(ax, ay, aw, ah, bx, by, bw, bh);
+
+
+
+
+#NMS
+@cython.boundscheck(False) # turn off bounds-checking for entire function
+@cython.wraparound(False)  # turn off negative index wrapping for entire function
+@cython.cdivision(True)
+cdef NMS(float[:, ::1] final_probs , float[:, ::1] final_bbox):
+    cdef list boxes = list()
+    cdef set indices = set()
+    cdef:
+        np.intp_t pred_length,class_length,class_loop,index,index2
+
+  
+    pred_length = final_bbox.shape[0]
+    class_length = final_probs.shape[1]
+    for class_loop in range(class_length):
+        for index in range(pred_length):
+            if final_probs[index,class_loop] == 0: continue
+            for index2 in range(index+1,pred_length):
+                if final_probs[index2,class_loop] == 0: continue
+                if index==index2 : continue
+                if box_iou_c(final_bbox[index,0],final_bbox[index,1],final_bbox[index,2],final_bbox[index,3],final_bbox[index2,0],final_bbox[index2,1],final_bbox[index2,2],final_bbox[index2,3]) >= 0.4:
+                    if final_probs[index2,class_loop] > final_probs[index, class_loop] :
+                        final_probs[index, class_loop] =0
+                        break
+                    final_probs[index2,class_loop]=0
+            
+            if index not in indices:
+                bb=BoundBox(class_length)
+                bb.x = final_bbox[index, 0]
+                bb.y = final_bbox[index, 1]
+                bb.w = final_bbox[index, 2]
+                bb.h = final_bbox[index, 3]
+                bb.c = final_bbox[index, 4]
+                bb.probs = np.asarray(final_probs[index,:])
+                boxes.append(bb)
+                indices.add(index)
+    return boxes
+
+# cdef NMS(float[:, ::1] final_probs , float[:, ::1] final_bbox):
+#     cdef list boxes = list()
+#     cdef:
+#         np.intp_t pred_length,class_length,class_loop,index,index2, i, j
+
+  
+#     pred_length = final_bbox.shape[0]
+#     class_length = final_probs.shape[1]
+
+#     for class_loop in range(class_length):
+#         order = np.argsort(final_probs[:,class_loop])[::-1]
+#         # First box
+#         for i in range(pred_length):
+#             index = order[i]
+#             if final_probs[index, class_loop] == 0.: 
+#                 continue
+#             # Second box
+#             for j in range(i+1, pred_length):
+#                 index2 = order[j]
+#                 if box_iou_c(
+#                     final_bbox[index,0],final_bbox[index,1],
+#                     final_bbox[index,2],final_bbox[index,3],
+#                     final_bbox[index2,0],final_bbox[index2,1],
+#                     final_bbox[index2,2],final_bbox[index2,3]) >= 0.4:
+#                     final_probs[index2, class_loop] = 0.
+                    
+#             bb = BoundBox(class_length)
+#             bb.x = final_bbox[index, 0]
+#             bb.y = final_bbox[index, 1]
+#             bb.w = final_bbox[index, 2]
+#             bb.h = final_bbox[index, 3]
+#             bb.c = final_bbox[index, 4]
+#             bb.probs = np.asarray(final_probs[index,:])
+#             boxes.append(bb)
+  
+#     return boxes
diff --git a/darkflow/dark/connected.py b/darkflow/dark/connected.py
new file mode 100644
index 0000000..d1eb37d
--- /dev/null
+++ b/darkflow/dark/connected.py
@@ -0,0 +1,111 @@
+from .layer import Layer
+import numpy as np
+
+class extract_layer(Layer):
+    def setup(self, old_inp, old_out,
+              activation, inp, out):
+        if inp is None: inp = range(old_inp)
+        self.activation = activation
+        self.old_inp = old_inp
+        self.old_out = old_out
+        self.inp = inp
+        self.out = out
+        self.wshape = {
+            'biases': [len(self.out)],
+            'weights': [len(self.inp), len(self.out)]
+        }
+
+    @property
+    def signature(self):
+        sig = ['connected']
+        sig += self._signature[1:-2]
+        return sig
+
+    def present(self):
+        args = self.signature
+        self.presenter = connected_layer(*args)
+
+    def recollect(self, val):
+        w = val['weights']
+        b = val['biases']
+        if w is None: self.w = val; return
+        w = np.take(w, self.inp, 0)
+        w = np.take(w, self.out, 1)
+        b = np.take(b, self.out)
+        assert1 = w.shape == tuple(self.wshape['weights'])
+        assert2 = b.shape == tuple(self.wshape['biases'])
+        assert assert1 and assert2, \
+        'Dimension does not match in {} recollect'.format(
+            self._signature)
+        
+        self.w['weights'] = w
+        self.w['biases'] = b
+    
+
+
+class select_layer(Layer):
+    def setup(self, inp, old, 
+              activation, inp_idx,
+              out, keep, train):
+        self.old = old
+        self.keep = keep
+        self.train = train
+        self.inp_idx = inp_idx
+        self.activation = activation
+        inp_dim = inp
+        if inp_idx is not None:
+            inp_dim = len(inp_idx)
+        self.inp = inp_dim
+        self.out = out
+        self.wshape = {
+            'biases': [out],
+            'weights': [inp_dim, out]
+        }
+
+    @property
+    def signature(self):
+        sig = ['connected']
+        sig += self._signature[1:-4]
+        return sig
+
+    def present(self):
+        args = self.signature
+        self.presenter = connected_layer(*args)
+
+    def recollect(self, val):
+        w = val['weights']
+        b = val['biases']
+        if w is None: self.w = val; return
+        if self.inp_idx is not None:
+            w = np.take(w, self.inp_idx, 0)
+            
+        keep_b = np.take(b, self.keep)
+        keep_w = np.take(w, self.keep, 1)
+        train_b = b[self.train:]
+        train_w = w[:, self.train:]
+        self.w['biases'] = np.concatenate(
+            (keep_b, train_b), axis = 0)
+        self.w['weights'] = np.concatenate(
+            (keep_w, train_w), axis = 1)
+
+
+class connected_layer(Layer):
+    def setup(self, input_size, 
+              output_size, activation):
+        self.activation = activation
+        self.inp = input_size
+        self.out = output_size
+        self.wshape = {
+            'biases': [self.out],
+            'weights': [self.inp, self.out]
+        }
+
+    def finalize(self, transpose):
+        weights = self.w['weights']
+        if weights is None: return
+        shp = self.wshape['weights']
+        if not transpose:
+            weights = weights.reshape(shp[::-1])
+            weights = weights.transpose([1,0])
+        else: weights = weights.reshape(shp)
+        self.w['weights'] = weights
\ No newline at end of file
diff --git a/darkflow/dark/convolution.py b/darkflow/dark/convolution.py
new file mode 100644
index 0000000..9e7141e
--- /dev/null
+++ b/darkflow/dark/convolution.py
@@ -0,0 +1,156 @@
+from .layer import Layer
+import numpy as np
+
+class local_layer(Layer):
+    def setup(self, ksize, c, n, stride, 
+              pad, w_, h_, activation):
+        self.pad = pad * int(ksize / 2)
+        self.activation = activation
+        self.stride = stride
+        self.ksize = ksize
+        self.h_out = h_
+        self.w_out = w_
+
+        self.dnshape = [h_ * w_, n, c, ksize, ksize]
+        self.wshape = dict({
+            'biases': [h_ * w_ * n],
+            'kernels': [h_ * w_, ksize, ksize, c, n]
+        })
+
+    def finalize(self, _):
+        weights = self.w['kernels']
+        if weights is None: return
+        weights = weights.reshape(self.dnshape)
+        weights = weights.transpose([0,3,4,2,1])
+        self.w['kernels'] = weights
+
+class conv_extract_layer(Layer):
+    def setup(self, ksize, c, n, stride, 
+              pad, batch_norm, activation,
+              inp, out):
+        if inp is None: inp = range(c)
+        self.activation = activation
+        self.batch_norm = batch_norm
+        self.stride = stride
+        self.ksize = ksize
+        self.pad = pad
+        self.inp = inp
+        self.out = out
+        self.wshape = dict({
+            'biases': [len(out)], 
+            'kernel': [ksize, ksize, len(inp), len(out)]
+        })
+
+    @property
+    def signature(self):
+        sig = ['convolutional']
+        sig += self._signature[1:-2]
+        return sig
+
+    def present(self):
+        args = self.signature
+        self.presenter = convolutional_layer(*args)
+
+    def recollect(self, w):
+        if w is None:
+            self.w = w
+            return
+        k = w['kernel']
+        b = w['biases']
+        k = np.take(k, self.inp, 2)
+        k = np.take(k, self.out, 3)
+        b = np.take(b, self.out)
+        assert1 = k.shape == tuple(self.wshape['kernel'])
+        assert2 = b.shape == tuple(self.wshape['biases'])
+        assert assert1 and assert2, \
+        'Dimension not matching in {} recollect'.format(
+            self._signature)
+        self.w['kernel'] = k
+        self.w['biases'] = b
+
+
+class conv_select_layer(Layer):
+    def setup(self, ksize, c, n, stride, 
+              pad, batch_norm, activation,
+              keep_idx, real_n):
+        self.batch_norm = bool(batch_norm)
+        self.activation = activation
+        self.keep_idx = keep_idx
+        self.stride = stride
+        self.ksize = ksize
+        self.pad = pad
+        self.wshape = dict({
+            'biases': [real_n], 
+            'kernel': [ksize, ksize, c, real_n]
+        })
+        if self.batch_norm:
+            self.wshape.update({
+                'moving_variance'  : [real_n], 
+                'moving_mean': [real_n], 
+                'gamma' : [real_n]
+            })
+            self.h['is_training'] = {
+                'shape': (),
+                'feed': True,
+                'dfault': False
+            }
+
+    @property
+    def signature(self):
+        sig = ['convolutional']
+        sig += self._signature[1:-2]
+        return sig
+    
+    def present(self):
+        args = self.signature
+        self.presenter = convolutional_layer(*args)
+
+    def recollect(self, w):
+        if w is None:
+            self.w = w
+            return
+        idx = self.keep_idx
+        k = w['kernel']
+        b = w['biases']
+        self.w['kernel'] = np.take(k, idx, 3) 
+        self.w['biases'] = np.take(b, idx)
+        if self.batch_norm:
+            m = w['moving_mean']
+            v = w['moving_variance']
+            g = w['gamma']
+            self.w['moving_mean'] = np.take(m, idx)
+            self.w['moving_variance'] = np.take(v, idx)
+            self.w['gamma'] = np.take(g, idx)
+
+class convolutional_layer(Layer):
+    def setup(self, ksize, c, n, stride, 
+              pad, batch_norm, activation):
+        self.batch_norm = bool(batch_norm)
+        self.activation = activation
+        self.stride = stride
+        self.ksize = ksize
+        self.pad = pad
+        self.dnshape = [n, c, ksize, ksize] # darknet shape
+        self.wshape = dict({
+            'biases': [n], 
+            'kernel': [ksize, ksize, c, n]
+        })
+        if self.batch_norm:
+            self.wshape.update({
+                'moving_variance'  : [n], 
+                'moving_mean': [n], 
+                'gamma' : [n]
+            })
+            self.h['is_training'] = {
+                'feed': True,
+                'dfault': False,
+                'shape': ()
+            }
+
+    def finalize(self, _):
+        """deal with darknet"""
+        kernel = self.w['kernel']
+        if kernel is None: return
+        kernel = kernel.reshape(self.dnshape)
+        kernel = kernel.transpose([2,3,1,0])
+        self.w['kernel'] = kernel
\ No newline at end of file
diff --git a/darkflow/dark/darknet.py b/darkflow/dark/darknet.py
new file mode 100644
index 0000000..8412456
--- /dev/null
+++ b/darkflow/dark/darknet.py
@@ -0,0 +1,86 @@
+from ..utils.process import cfg_yielder
+from .darkop import create_darkop
+from ..utils import loader
+import warnings
+import time
+import os
+
+class Darknet(object):
+
+    _EXT = '.weights'
+
+    def __init__(self, FLAGS):
+        self.get_weight_src(FLAGS)
+        self.modify = False
+
+        print('Parsing {}'.format(self.src_cfg))
+        src_parsed = self.parse_cfg(self.src_cfg, FLAGS)
+        self.src_meta, self.src_layers = src_parsed
+        
+        if self.src_cfg == FLAGS.model:
+            self.meta, self.layers = src_parsed
+        else: 
+        	print('Parsing {}'.format(FLAGS.model))
+        	des_parsed = self.parse_cfg(FLAGS.model, FLAGS)
+        	self.meta, self.layers = des_parsed
+
+        self.load_weights()
+
+    def get_weight_src(self, FLAGS):
+        """
+        analyse FLAGS.load to know where is the 
+        source binary and what is its config.
+        can be: None, FLAGS.model, or some other
+        """
+        self.src_bin = FLAGS.model + self._EXT
+        self.src_bin = FLAGS.binary + self.src_bin
+        self.src_bin = os.path.abspath(self.src_bin)
+        exist = os.path.isfile(self.src_bin)
+
+        if FLAGS.load == str(): FLAGS.load = int()
+        if type(FLAGS.load) is int:
+            self.src_cfg = FLAGS.model
+            if FLAGS.load: self.src_bin = None
+            elif not exist: self.src_bin = None
+        else:
+            assert os.path.isfile(FLAGS.load), \
+            '{} not found'.format(FLAGS.load)
+            self.src_bin = FLAGS.load
+            name = loader.model_name(FLAGS.load)
+            cfg_path = os.path.join(FLAGS.config, name + '.cfg')
+            if not os.path.isfile(cfg_path):
+                warnings.warn(
+                    '{} not found, use {} instead'.format(
+                    cfg_path, FLAGS.model))
+                cfg_path = FLAGS.model
+            self.src_cfg = cfg_path
+            FLAGS.load = int()
+
+
+    def parse_cfg(self, model, FLAGS):
+        """
+        return a list of `layers` objects (darkop.py)
+        given path to binaries/ and configs/
+        """
+        args = [model, FLAGS.binary]
+        cfg_layers = cfg_yielder(*args)
+        meta = dict(); layers = list()
+        for i, info in enumerate(cfg_layers):
+            if i == 0: meta = info; continue
+            else: new = create_darkop(*info)
+            layers.append(new)
+        return meta, layers
+
+    def load_weights(self):
+        """
+        Use `layers` and Loader to load .weights file
+        """
+        print('Loading {} ...'.format(self.src_bin))
+        start = time.time()
+
+        args = [self.src_bin, self.src_layers]
+        wgts_loader = loader.create_loader(*args)
+        for layer in self.layers: layer.load(wgts_loader)
+        
+        stop = time.time()
+        print('Finished in {}s'.format(stop - start))
\ No newline at end of file
diff --git a/darkflow/dark/darkop.py b/darkflow/dark/darkop.py
new file mode 100644
index 0000000..bcde00a
--- /dev/null
+++ b/darkflow/dark/darkop.py
@@ -0,0 +1,60 @@
+from .layer import Layer
+from .convolution import *
+from .connected import *
+
+class avgpool_layer(Layer):
+    pass
+
+class crop_layer(Layer):
+    pass
+
+class maxpool_layer(Layer):
+    def setup(self, ksize, stride, pad):
+        self.stride = stride
+        self.ksize = ksize
+        self.pad = pad
+
+class softmax_layer(Layer):
+    def setup(self, groups):
+        self.groups = groups
+
+class dropout_layer(Layer):
+    def setup(self, p):
+        self.h['pdrop'] = dict({
+            'feed': p, # for training
+            'dfault': 1.0, # for testing
+            'shape': ()
+        })
+
+class route_layer(Layer):
+    def setup(self, routes):
+        self.routes = routes
+
+class reorg_layer(Layer):
+    def setup(self, stride):
+        self.stride = stride
+
+"""
+Darkop Factory
+"""
+
+darkops = {
+    'dropout': dropout_layer,
+    'connected': connected_layer,
+    'maxpool': maxpool_layer,
+    'convolutional': convolutional_layer,
+    'avgpool': avgpool_layer,
+    'softmax': softmax_layer,
+    'crop': crop_layer,
+    'local': local_layer,
+    'select': select_layer,
+    'route': route_layer,
+    'reorg': reorg_layer,
+    'conv-select': conv_select_layer,
+    'conv-extract': conv_extract_layer,
+    'extract': extract_layer
+}
+
+def create_darkop(ltype, num, *args):
+    op_class = darkops.get(ltype, Layer)
+    return op_class(ltype, num, *args)
\ No newline at end of file
diff --git a/darkflow/dark/layer.py b/darkflow/dark/layer.py
new file mode 100644
index 0000000..08dee9a
--- /dev/null
+++ b/darkflow/dark/layer.py
@@ -0,0 +1,71 @@
+from ..utils import loader
+import numpy as np
+
+class Layer(object):
+
+    def __init__(self, *args):
+        self._signature = list(args)
+        self.type = list(args)[0]
+        self.number = list(args)[1]
+
+        self.w = dict() # weights
+        self.h = dict() # placeholders
+        self.wshape = dict() # weight shape
+        self.wsize = dict() # weight size
+        self.setup(*args[2:]) # set attr up
+        self.present()
+        for var in self.wshape:
+            shp = self.wshape[var]
+            size = np.prod(shp)
+            self.wsize[var] = size
+
+    def load(self, src_loader):
+        var_lay = src_loader.VAR_LAYER
+        if self.type not in var_lay: return
+
+        src_type = type(src_loader)
+        if src_type is loader.weights_loader:
+            wdict = self.load_weights(src_loader)
+        else: 
+            wdict = self.load_ckpt(src_loader)
+        if wdict is not None:
+            self.recollect(wdict)
+
+    def load_weights(self, src_loader):
+        val = src_loader([self.presenter])
+        if val is None: return None
+        else: return val.w
+
+    def load_ckpt(self, src_loader):
+        result = dict()
+        presenter = self.presenter
+        for var in presenter.wshape:
+            name = presenter.varsig(var)
+            shape = presenter.wshape[var]
+            key = [name, shape]
+            val = src_loader(key)
+            result[var] = val
+        return result
+
+    @property
+    def signature(self):
+        return self._signature
+
+    # For comparing two layers
+    def __eq__(self, other):
+        return self.signature == other.signature
+    def __ne__(self, other):
+        return not self.__eq__(other)
+
+    def varsig(self, var):
+        if var not in self.wshape:
+            return None
+        sig = str(self.number)
+        sig += '-' + self.type
+        sig += '/' + var
+        return sig
+
+    def recollect(self, w): self.w = w
+    def present(self): self.presenter = self
+    def setup(self, *args): pass
+    def finalize(self): pass 
\ No newline at end of file
diff --git a/darkflow/defaults.py b/darkflow/defaults.py
new file mode 100644
index 0000000..566bea0
--- /dev/null
+++ b/darkflow/defaults.py
@@ -0,0 +1,96 @@
+class argHandler(dict):
+    #A super duper fancy custom made CLI argument handler!!
+    __getattr__ = dict.get
+    __setattr__ = dict.__setitem__
+    __delattr__ = dict.__delitem__
+    _descriptions = {'help, --h, -h': 'show this super helpful message and exit'}
+    
+    def setDefaults(self):
+        self.define('imgdir', './sample_img/', 'path to testing directory with images')
+        self.define('binary', './bin/', 'path to .weights directory')
+        self.define('config', './cfg/', 'path to .cfg directory')
+        self.define('dataset', '../pascal/VOCdevkit/IMG/', 'path to dataset directory')
+        self.define('labels', 'labels.txt', 'path to labels file')
+        self.define('backup', './ckpt/', 'path to backup folder')
+        self.define('summary', './summary/', 'path to TensorBoard summaries directory')
+        self.define('annotation', '../pascal/VOCdevkit/ANN/', 'path to annotation directory')
+        self.define('threshold', -0.1, 'detection threshold')
+        self.define('model', '', 'configuration of choice')
+        self.define('trainer', 'rmsprop', 'training algorithm')
+        self.define('momentum', 0.0, 'applicable for rmsprop and momentum optimizers')
+        self.define('verbalise', True, 'say out loud while building graph')
+        self.define('train', False, 'train the whole net')
+        self.define('load', '', 'how to initialize the net? Either from .weights or a checkpoint, or even from scratch')
+        self.define('savepb', False, 'save net and weight to a .pb file')
+        self.define('gpu', 0.0, 'how much gpu (from 0.0 to 1.0)')
+        self.define('gpuName', '/gpu:0', 'GPU device name')
+        self.define('lr', 1e-5, 'learning rate')
+        self.define('keep',20,'Number of most recent training results to save')
+        self.define('batch', 16, 'batch size')
+        self.define('epoch', 1000, 'number of epoch')
+        self.define('save', 2000, 'save checkpoint every ? training examples')
+        self.define('demo', '', 'demo on webcam')
+        self.define('queue', 1, 'process demo in batch')
+        self.define('json', False, 'Outputs bounding box information in json format.')
+        self.define('saveVideo', False, 'Records video from input video or camera')
+        self.define('pbLoad', '', 'path to .pb protobuf file (metaLoad must also be specified)')
+        self.define('metaLoad', '', 'path to .meta file generated during --savepb that corresponds to .pb file')
+
+    def define(self, argName, default, description):
+        self[argName] = default
+        self._descriptions[argName] = description
+    
+    def help(self):
+        print('Example usage: flow --imgdir sample_img/ --model cfg/yolo.cfg --load bin/yolo.weights')
+        print('')
+        print('Arguments:')
+        spacing = max([len(i) for i in self._descriptions.keys()]) + 2
+        for item in self._descriptions:
+            currentSpacing = spacing - len(item)
+            print('  --' + item + (' ' * currentSpacing) + self._descriptions[item])
+        print('')
+        exit()
+
+    def parseArgs(self, args):
+        print('')
+        i = 1
+        while i < len(args):
+            if args[i] == '-h' or args[i] == '--h' or args[i] == '--help':
+                self.help() #Time for some self help! :)
+            if len(args[i]) < 2:
+                print('ERROR - Invalid argument: ' + args[i])
+                print('Try running flow --help')
+                exit()
+            argumentName = args[i][2:]
+            if isinstance(self.get(argumentName), bool):
+                if not (i + 1) >= len(args) and (args[i + 1].lower() != 'false' and args[i + 1].lower() != 'true') and not args[i + 1].startswith('--'):
+                    print('ERROR - Expected boolean value (or no value) following argument: ' + args[i])
+                    print('Try running flow --help')
+                    exit()
+                elif not (i + 1) >= len(args) and (args[i + 1].lower() == 'false' or args[i + 1].lower() == 'true'):
+                    self[argumentName] = (args[i + 1].lower() == 'true')
+                    i += 1
+                else:
+                    self[argumentName] = True
+            elif args[i].startswith('--') and not (i + 1) >= len(args) and not args[i + 1].startswith('--') and argumentName in self:
+                if isinstance(self[argumentName], float):
+                    try:
+                        args[i + 1] = float(args[i + 1])
+                    except:
+                        print('ERROR - Expected float for argument: ' + args[i])
+                        print('Try running flow --help')
+                        exit()
+                elif isinstance(self[argumentName], int):
+                    try:
+                        args[i + 1] = int(args[i + 1])
+                    except:
+                        print('ERROR - Expected int for argument: ' + args[i])
+                        print('Try running flow --help')
+                        exit()
+                self[argumentName] = args[i + 1]
+                i += 1
+            else:
+                print('ERROR - Invalid argument: ' + args[i])
+                print('Try running flow --help')
+                exit()
+            i += 1
diff --git a/darkflow/net/build.py b/darkflow/net/build.py
new file mode 100644
index 0000000..f416161
--- /dev/null
+++ b/darkflow/net/build.py
@@ -0,0 +1,177 @@
+import tensorflow as tf
+import time
+from . import help
+from . import flow
+from .ops import op_create, identity
+from .ops import HEADER, LINE
+from .framework import create_framework
+from ..dark.darknet import Darknet
+import json
+import os
+
+class TFNet(object):
+
+	_TRAINER = dict({
+		'rmsprop': tf.train.RMSPropOptimizer,
+		'adadelta': tf.train.AdadeltaOptimizer,
+		'adagrad': tf.train.AdagradOptimizer,
+		'adagradDA': tf.train.AdagradDAOptimizer,
+		'momentum': tf.train.MomentumOptimizer,
+		'adam': tf.train.AdamOptimizer,
+		'ftrl': tf.train.FtrlOptimizer,
+		'sgd': tf.train.GradientDescentOptimizer
+	})
+
+	# imported methods
+	_get_fps = help._get_fps
+	say = help.say
+	train = flow.train
+	camera = help.camera
+	predict = flow.predict
+	return_predict = flow.return_predict
+	to_darknet = help.to_darknet
+	build_train_op = help.build_train_op
+	load_from_ckpt = help.load_from_ckpt
+
+	def __init__(self, FLAGS, darknet = None):
+		self.ntrain = 0
+
+		if isinstance(FLAGS, dict):
+			from ..defaults import argHandler
+			newFLAGS = argHandler()
+			newFLAGS.setDefaults()
+			newFLAGS.update(FLAGS)
+			FLAGS = newFLAGS
+
+		self.FLAGS = FLAGS
+		if self.FLAGS.pbLoad and self.FLAGS.metaLoad:
+			self.say('\nLoading from .pb and .meta')
+			self.graph = tf.Graph()
+			device_name = FLAGS.gpuName \
+				if FLAGS.gpu > 0.0 else None
+			with tf.device(device_name):
+				with self.graph.as_default() as g:
+					self.build_from_pb()
+			return
+
+		if darknet is None:	
+			darknet = Darknet(FLAGS)
+			self.ntrain = len(darknet.layers)
+
+		self.darknet = darknet
+		args = [darknet.meta, FLAGS]
+		self.num_layer = len(darknet.layers)
+		self.framework = create_framework(*args)
+		
+		self.meta = darknet.meta
+
+		self.say('\nBuilding net ...')
+		start = time.time()
+		self.graph = tf.Graph()
+		device_name = FLAGS.gpuName \
+			if FLAGS.gpu > 0.0 else None
+		with tf.device(device_name):
+			with self.graph.as_default() as g:
+				self.build_forward()
+				self.setup_meta_ops()
+		self.say('Finished in {}s\n'.format(
+			time.time() - start))
+	
+	def build_from_pb(self):
+		with tf.gfile.FastGFile(self.FLAGS.pbLoad, "rb") as f:
+			graph_def = tf.GraphDef()
+			graph_def.ParseFromString(f.read())
+		
+		tf.import_graph_def(
+			graph_def,
+			name=""
+		)
+		with open(self.FLAGS.metaLoad, 'r') as fp:
+			self.meta = json.load(fp)
+		self.framework = create_framework(self.meta, self.FLAGS)
+
+		# Placeholders
+		self.inp = tf.get_default_graph().get_tensor_by_name('input:0')
+		self.feed = dict() # other placeholders
+		self.out = tf.get_default_graph().get_tensor_by_name('output:0')
+		
+		self.setup_meta_ops()
+	
+	def build_forward(self):
+		verbalise = self.FLAGS.verbalise
+
+		# Placeholders
+		inp_size = [None] + self.meta['inp_size']
+		self.inp = tf.placeholder(tf.float32, inp_size, 'input')
+		self.feed = dict() # other placeholders
+
+		# Build the forward pass
+		state = identity(self.inp)
+		roof = self.num_layer - self.ntrain
+		self.say(HEADER, LINE)
+		for i, layer in enumerate(self.darknet.layers):
+			scope = '{}-{}'.format(str(i),layer.type)
+			args = [layer, state, i, roof, self.feed]
+			state = op_create(*args)
+			mess = state.verbalise()
+			self.say(mess)
+		self.say(LINE)
+
+		self.top = state
+		self.out = tf.identity(state.out, name='output')
+
+	def setup_meta_ops(self):
+		cfg = dict({
+			'allow_soft_placement': False,
+			'log_device_placement': False
+		})
+
+		utility = min(self.FLAGS.gpu, 1.)
+		if utility > 0.0:
+			self.say('GPU mode with {} usage'.format(utility))
+			cfg['gpu_options'] = tf.GPUOptions(
+				per_process_gpu_memory_fraction = utility)
+			cfg['allow_soft_placement'] = True
+		else: 
+			self.say('Running entirely on CPU')
+			cfg['device_count'] = {'GPU': 0}
+
+		if self.FLAGS.train: self.build_train_op()
+		
+		if self.FLAGS.summary is not None:
+			self.summary_op = tf.summary.merge_all()
+			self.writer = tf.summary.FileWriter(self.FLAGS.summary + 'train')
+		
+		self.sess = tf.Session(config = tf.ConfigProto(**cfg))
+		self.sess.run(tf.global_variables_initializer())
+
+		if not self.ntrain: return
+		self.saver = tf.train.Saver(tf.global_variables(), 
+			max_to_keep = self.FLAGS.keep)
+		if self.FLAGS.load != 0: self.load_from_ckpt()
+		
+		if self.FLAGS.summary is not None:
+			self.writer.add_graph(self.sess.graph)
+
+	def savepb(self):
+		"""
+		Create a standalone const graph def that 
+		C++	can load and run.
+		"""
+		darknet_pb = self.to_darknet()
+		flags_pb = self.FLAGS
+		flags_pb.verbalise = False
+		
+		flags_pb.train = False
+		# rebuild another tfnet. all const.
+		tfnet_pb = TFNet(flags_pb, darknet_pb)		
+		tfnet_pb.sess = tf.Session(graph = tfnet_pb.graph)
+		# tfnet_pb.predict() # uncomment for unit testing
+		name = 'built_graph/{}.pb'.format(self.meta['name'])
+		os.makedirs(os.path.dirname(name), exist_ok=True)
+		#Save dump of everything in meta
+		with open('built_graph/{}.meta'.format(self.meta['name']), 'w') as fp:
+			json.dump(self.meta, fp)
+		self.say('Saving const graph def to {}'.format(name))
+		graph_def = tfnet_pb.sess.graph_def
+		tf.train.write_graph(graph_def,'./', name, False)
\ No newline at end of file
diff --git a/darkflow/net/flow.py b/darkflow/net/flow.py
new file mode 100644
index 0000000..aa1d8f4
--- /dev/null
+++ b/darkflow/net/flow.py
@@ -0,0 +1,148 @@
+import os
+import time
+import numpy as np
+import tensorflow as tf
+import pickle
+from multiprocessing.pool import ThreadPool
+
+train_stats = (
+    'Training statistics: \n'
+    '\tLearning rate : {}\n'
+    '\tBatch size    : {}\n'
+    '\tEpoch number  : {}\n'
+    '\tBackup every  : {}'
+)
+pool = ThreadPool()
+
+def _save_ckpt(self, step, loss_profile):
+    file = '{}-{}{}'
+    model = self.meta['name']
+
+    profile = file.format(model, step, '.profile')
+    profile = os.path.join(self.FLAGS.backup, profile)
+    with open(profile, 'wb') as profile_ckpt: 
+        pickle.dump(loss_profile, profile_ckpt)
+
+    ckpt = file.format(model, step, '')
+    ckpt = os.path.join(self.FLAGS.backup, ckpt)
+    self.say('Checkpoint at step {}'.format(step))
+    self.saver.save(self.sess, ckpt)
+
+
+def train(self):
+    loss_ph = self.framework.placeholders
+    loss_mva = None; profile = list()
+
+    batches = self.framework.shuffle()
+    loss_op = self.framework.loss
+
+    for i, (x_batch, datum) in enumerate(batches):
+        if not i: self.say(train_stats.format(
+            self.FLAGS.lr, self.FLAGS.batch,
+            self.FLAGS.epoch, self.FLAGS.save
+        ))
+
+        feed_dict = {
+            loss_ph[key]: datum[key] 
+                for key in loss_ph }
+        feed_dict[self.inp] = x_batch
+        feed_dict.update(self.feed)
+
+        fetches = [self.train_op, loss_op, self.summary_op] 
+        fetched = self.sess.run(fetches, feed_dict)
+        loss = fetched[1]
+
+        if loss_mva is None: loss_mva = loss
+        loss_mva = .9 * loss_mva + .1 * loss
+        step_now = self.FLAGS.load + i + 1
+
+        self.writer.add_summary(fetched[2], step_now)
+
+        form = 'step {} - loss {} - moving ave loss {}'
+        self.say(form.format(step_now, loss, loss_mva))
+        profile += [(loss, loss_mva)]
+
+        ckpt = (i+1) % (self.FLAGS.save // self.FLAGS.batch)
+        args = [step_now, profile]
+        if not ckpt: _save_ckpt(self, *args)
+
+    if ckpt: _save_ckpt(self, *args)
+
+def return_predict(self, im):
+    assert isinstance(im, np.ndarray), \
+				'Image is not a np.ndarray'
+    h, w, _ = im.shape
+    im = self.framework.resize_input(im)
+    this_inp = np.expand_dims(im, 0)
+    feed_dict = {self.inp : this_inp}
+
+    out = self.sess.run(self.out, feed_dict)[0]
+    boxes = self.framework.findboxes(out)
+    threshold = self.FLAGS.threshold
+    boxesInfo = list()
+    for box in boxes:
+        tmpBox = self.framework.process_box(box, h, w, threshold)
+        if tmpBox is None:
+            continue
+        boxesInfo.append({
+            "label": tmpBox[4],
+            "confidence": tmpBox[6],
+            "topleft": {
+                "x": tmpBox[0],
+                "y": tmpBox[2]},
+            "bottomright": {
+                "x": tmpBox[1],
+                "y": tmpBox[3]}
+        })
+    return boxesInfo
+
+import math
+
+def predict(self):
+    inp_path = self.FLAGS.imgdir
+    all_inps = os.listdir(inp_path)
+    all_inps = [i for i in all_inps if self.framework.is_inp(i)]
+    if not all_inps:
+        msg = 'Failed to find any images in {} .'
+        exit('Error: {}'.format(msg.format(inp_path)))
+
+    batch = min(self.FLAGS.batch, len(all_inps))
+
+    # predict in batches
+    n_batch = int(math.ceil(len(all_inps) / batch))
+    for j in range(n_batch):
+        from_idx = j * batch
+        to_idx = min(from_idx + batch, len(all_inps))
+
+        # collect images input in the batch
+        inp_feed = list(); new_all = list()
+        this_batch = all_inps[from_idx:to_idx]
+        for inp in this_batch:
+            new_all += [inp]
+            this_inp = os.path.join(inp_path, inp)
+            this_inp = self.framework.preprocess(this_inp)
+            expanded = np.expand_dims(this_inp, 0)
+            inp_feed.append(expanded)
+        this_batch = new_all
+
+        # Feed to the net
+        feed_dict = {self.inp : np.concatenate(inp_feed, 0)}    
+        self.say('Forwarding {} inputs ...'.format(len(inp_feed)))
+        start = time.time()
+        out = self.sess.run(self.out, feed_dict)
+        stop = time.time(); last = stop - start
+        self.say('Total time = {}s / {} inps = {} ips'.format(
+            last, len(inp_feed), len(inp_feed) / last))
+
+        # Post processing
+        self.say('Post processing {} inputs ...'.format(len(inp_feed)))
+        start = time.time()
+        pool.map(lambda p: (lambda i, prediction:
+            self.framework.postprocess(
+               prediction, os.path.join(inp_path, this_batch[i])))(*p),
+            enumerate(out))
+        stop = time.time(); last = stop - start
+
+        # Timing
+        self.say('Total time = {}s / {} inps = {} ips'.format(
+            last, len(inp_feed), len(inp_feed) / last))
diff --git a/darkflow/net/framework.py b/darkflow/net/framework.py
new file mode 100644
index 0000000..627629e
--- /dev/null
+++ b/darkflow/net/framework.py
@@ -0,0 +1,59 @@
+from . import yolo
+from . import yolov2
+from . import vanilla
+from os.path import basename
+
+class framework(object):
+    constructor = vanilla.constructor
+    loss = vanilla.train.loss
+    
+    def __init__(self, meta, FLAGS):
+        model = basename(meta['model'])
+        model = '.'.join(model.split('.')[:-1])
+        meta['name'] = model
+        
+        self.constructor(meta, FLAGS)
+
+    def is_inp(self, file_name):
+        return True
+
+class YOLO(framework):
+    constructor = yolo.constructor
+    parse = yolo.data.parse
+    shuffle = yolo.data.shuffle
+    preprocess = yolo.predict.preprocess
+    postprocess = yolo.predict.postprocess
+    loss = yolo.train.loss
+    is_inp = yolo.misc.is_inp
+    profile = yolo.misc.profile
+    _batch = yolo.data._batch
+    resize_input = yolo.predict.resize_input
+    findboxes = yolo.predict.findboxes
+    process_box = yolo.predict.process_box
+
+class YOLOv2(framework):
+    constructor = yolo.constructor
+    parse = yolo.data.parse
+    shuffle = yolov2.data.shuffle
+    preprocess = yolo.predict.preprocess
+    loss = yolov2.train.loss
+    is_inp = yolo.misc.is_inp
+    postprocess = yolov2.predict.postprocess
+    _batch = yolov2.data._batch
+    resize_input = yolo.predict.resize_input
+    findboxes = yolov2.predict.findboxes
+    process_box = yolo.predict.process_box
+
+"""
+framework factory
+"""
+
+types = {
+    '[detection]': YOLO,
+    '[region]': YOLOv2
+}
+
+def create_framework(meta, FLAGS):
+    net_type = meta['type']
+    this = types.get(net_type, framework)
+    return this(meta, FLAGS)
\ No newline at end of file
diff --git a/darkflow/net/help.py b/darkflow/net/help.py
new file mode 100644
index 0000000..24f1614
--- /dev/null
+++ b/darkflow/net/help.py
@@ -0,0 +1,171 @@
+"""
+tfnet secondary (helper) methods
+"""
+from ..utils.loader import create_loader
+from time import time as timer
+import tensorflow as tf
+import numpy as np
+import sys
+import cv2
+import os
+
+old_graph_msg = 'Resolving old graph def {} (no guarantee)'
+
+def build_train_op(self):
+    self.framework.loss(self.out)
+    self.say('Building {} train op'.format(self.meta['model']))
+    optimizer = self._TRAINER[self.FLAGS.trainer](self.FLAGS.lr)
+    gradients = optimizer.compute_gradients(self.framework.loss)
+    self.train_op = optimizer.apply_gradients(gradients)
+
+def load_from_ckpt(self):
+    if self.FLAGS.load < 0: # load lastest ckpt
+        with open(self.FLAGS.backup + 'checkpoint', 'r') as f:
+            last = f.readlines()[-1].strip()
+            load_point = last.split(' ')[1]
+            load_point = load_point.split('"')[1]
+            load_point = load_point.split('-')[-1]
+            self.FLAGS.load = int(load_point)
+    
+    load_point = os.path.join(self.FLAGS.backup, self.meta['name'])
+    load_point = '{}-{}'.format(load_point, self.FLAGS.load)
+    self.say('Loading from {}'.format(load_point))
+    try: self.saver.restore(self.sess, load_point)
+    except: load_old_graph(self, load_point)
+
+def say(self, *msgs):
+    if not self.FLAGS.verbalise:
+        return
+    msgs = list(msgs)
+    for msg in msgs:
+        if msg is None: continue
+        print(msg)
+
+def load_old_graph(self, ckpt): 
+    ckpt_loader = create_loader(ckpt)
+    self.say(old_graph_msg.format(ckpt))
+    
+    for var in tf.global_variables():
+        name = var.name.split(':')[0]
+        args = [name, var.get_shape()]
+        val = ckpt_loader(args)
+        assert val is not None, \
+        'Cannot find and load {}'.format(var.name)
+        shp = val.shape
+        plh = tf.placeholder(tf.float32, shp)
+        op = tf.assign(var, plh)
+        self.sess.run(op, {plh: val})
+
+def _get_fps(self, frame):
+    elapsed = int()
+    start = timer()
+    preprocessed = self.framework.preprocess(frame)
+    feed_dict = {self.inp: [preprocessed]}
+    net_out = self.sess.run(self.out, feed_dict)[0]
+    processed = self.framework.postprocess(net_out, frame, False)
+    return timer() - start
+
+def camera(self):
+    file = self.FLAGS.demo
+    SaveVideo = self.FLAGS.saveVideo
+    
+    if file == 'camera':
+        file = 0
+    else:
+        assert os.path.isfile(file), \
+        'file {} does not exist'.format(file)
+        
+    camera = cv2.VideoCapture(file)
+    
+    if file == 0:
+        self.say('Press [ESC] to quit demo')
+        
+    assert camera.isOpened(), \
+    'Cannot capture source'
+    
+    if file == 0:#camera window
+        cv2.namedWindow('', 0)
+        _, frame = camera.read()
+        height, width, _ = frame.shape
+        cv2.resizeWindow('', width, height)
+    else:
+        _, frame = camera.read()
+        height, width, _ = frame.shape
+
+    if SaveVideo:
+        fourcc = cv2.VideoWriter_fourcc(*'XVID')
+        if file == 0:#camera window
+          fps = 1 / self._get_fps(frame)
+          if fps < 1:
+            fps = 1
+        else:
+            fps = round(camera.get(cv2.CAP_PROP_FPS))
+        videoWriter = cv2.VideoWriter(
+            'video.avi', fourcc, fps, (width, height))
+
+    # buffers for demo in batch
+    buffer_inp = list()
+    buffer_pre = list()
+    
+    elapsed = int()
+    start = timer()
+    self.say('Press [ESC] to quit demo')
+    # Loop through frames
+    while camera.isOpened():
+        elapsed += 1
+        _, frame = camera.read()
+        if frame is None:
+            print ('\nEnd of Video')
+            break
+        preprocessed = self.framework.preprocess(frame)
+        buffer_inp.append(frame)
+        buffer_pre.append(preprocessed)
+        
+        # Only process and imshow when queue is full
+        if elapsed % self.FLAGS.queue == 0:
+            feed_dict = {self.inp: buffer_pre}
+            net_out = self.sess.run(self.out, feed_dict)
+            for img, single_out in zip(buffer_inp, net_out):
+                postprocessed = self.framework.postprocess(
+                    single_out, img, False)
+                if SaveVideo:
+                    videoWriter.write(postprocessed)
+                if file == 0: #camera window
+                    cv2.imshow('', postprocessed)
+            # Clear Buffers
+            buffer_inp = list()
+            buffer_pre = list()
+
+        if elapsed % 5 == 0:
+            sys.stdout.write('\r')
+            sys.stdout.write('{0:3.3f} FPS'.format(
+                elapsed / (timer() - start)))
+            sys.stdout.flush()
+        if file == 0: #camera window
+            choice = cv2.waitKey(1)
+            if choice == 27: break
+
+    sys.stdout.write('\n')
+    if SaveVideo:
+        videoWriter.release()
+    camera.release()
+    if file == 0: #camera window
+        cv2.destroyAllWindows()
+
+def to_darknet(self):
+    darknet_ckpt = self.darknet
+
+    with self.graph.as_default() as g:
+        for var in tf.global_variables():
+            name = var.name.split(':')[0]
+            var_name = name.split('-')
+            l_idx = int(var_name[0])
+            w_sig = var_name[1].split('/')[-1]
+            l = darknet_ckpt.layers[l_idx]
+            l.w[w_sig] = var.eval(self.sess)
+
+    for layer in darknet_ckpt.layers:
+        for ph in layer.h:
+            layer.h[ph] = None
+
+    return darknet_ckpt
diff --git a/darkflow/net/ops/__init__.py b/darkflow/net/ops/__init__.py
new file mode 100644
index 0000000..d687cb3
--- /dev/null
+++ b/darkflow/net/ops/__init__.py
@@ -0,0 +1,27 @@
+from .simple import *
+from .convolution import *
+from .baseop import HEADER, LINE
+
+op_types = {
+	'convolutional': convolutional,
+	'conv-select': conv_select,
+	'connected': connected,
+	'maxpool': maxpool,
+	'leaky': leaky,
+	'dropout': dropout,
+	'flatten': flatten,
+	'avgpool': avgpool,
+	'softmax': softmax,
+	'identity': identity,
+	'crop': crop,
+	'local': local,
+	'select': select,
+	'route': route,
+	'reorg': reorg,
+	'conv-extract': conv_extract,
+	'extract': extract
+}
+
+def op_create(*args):
+	layer_type = list(args)[0].type
+	return op_types[layer_type](*args)
\ No newline at end of file
diff --git a/darkflow/net/ops/baseop.py b/darkflow/net/ops/baseop.py
new file mode 100644
index 0000000..8992aca
--- /dev/null
+++ b/darkflow/net/ops/baseop.py
@@ -0,0 +1,100 @@
+import tensorflow as tf
+import numpy as np
+
+FORM = '{:>6} | {:>6} | {:<32} | {}'
+FORM_ = '{}+{}+{}+{}'
+LINE = FORM_.format('-'*7, '-'*8, '-'*34, '-'*15) 
+HEADER = FORM.format(
+    'Source', 'Train?','Layer description', 'Output size')
+
+def _shape(tensor): # work for both tf.Tensor & np.ndarray
+    if type(tensor) in [tf.Variable, tf.Tensor]: 
+        return tensor.get_shape()
+    else: return tensor.shape
+
+def _name(tensor):
+    return tensor.name.split(':')[0]
+
+class BaseOp(object):
+    """
+    BaseOp objects initialise with a darknet's `layer` object
+    and input tensor of that layer `inp`, it calculates the 
+    output of this layer and place the result in self.out
+    """
+
+    # let slim take care of the following vars
+    _SLIM = ['gamma', 'moving_mean', 'moving_variance']
+
+    def __init__(self, layer, inp, num, roof, feed):
+        self.inp = inp # BaseOp
+        self.num = num # int
+        self.out = None # tf.Tensor
+        self.lay = layer
+
+        self.scope = '{}-{}'.format(
+            str(self.num), self.lay.type)
+        self.gap = roof - self.num
+        self.var = not self.gap > 0
+        self.act = 'Load '
+        self.convert(feed)
+        if self.var: self.train_msg = 'Yep! '
+        else: self.train_msg = 'Nope '
+        self.forward()
+
+    def convert(self, feed):
+        """convert self.lay to variables & placeholders"""
+        for var in self.lay.wshape:
+            self.wrap_variable(var)
+        for ph in self.lay.h:
+            self.wrap_pholder(ph, feed)
+
+    def wrap_variable(self, var):
+        """wrap layer.w into variables"""
+        val = self.lay.w.get(var, None)
+        if val is None:
+            shape = self.lay.wshape[var]
+            args = [0., 1e-2, shape]
+            if 'moving_mean' in var:
+                val = np.zeros(shape)
+            elif 'moving_variance' in var:
+                val = np.ones(shape)
+            else:
+                val = np.random.normal(*args)
+            self.lay.w[var] = val.astype(np.float32)
+            self.act = 'Init '
+        if not self.var: return
+
+        val = self.lay.w[var]
+        self.lay.w[var] = tf.constant_initializer(val)
+        if var in self._SLIM: return
+        with tf.variable_scope(self.scope):
+            self.lay.w[var] = tf.get_variable(var,
+                shape = self.lay.wshape[var],
+                dtype = tf.float32,
+                initializer = self.lay.w[var])
+
+    def wrap_pholder(self, ph, feed):
+        """wrap layer.h into placeholders"""
+        phtype = type(self.lay.h[ph])
+        if phtype is not dict: return
+
+        sig = '{}/{}'.format(self.scope, ph)
+        val = self.lay.h[ph]
+
+        self.lay.h[ph] = tf.placeholder_with_default(
+            val['dfault'], val['shape'], name = sig)
+        feed[self.lay.h[ph]] = val['feed']
+
+    def verbalise(self): # console speaker
+        msg = str()
+        inp = _name(self.inp.out)
+        if inp == 'input': \
+        msg = FORM.format(
+            '', '', 'input',
+            _shape(self.inp.out)) + '\n'
+        if not self.act: return msg
+        return msg + FORM.format(
+            self.act, self.train_msg, 
+            self.speak(), _shape(self.out))
+    
+    def speak(self): pass
\ No newline at end of file
diff --git a/darkflow/net/ops/convolution.py b/darkflow/net/ops/convolution.py
new file mode 100644
index 0000000..167b0fd
--- /dev/null
+++ b/darkflow/net/ops/convolution.py
@@ -0,0 +1,116 @@
+import tensorflow.contrib.slim as slim
+from .baseop import BaseOp
+import tensorflow as tf
+import numpy as np
+
+class reorg(BaseOp):
+    def _forward(self):
+        inp = self.inp.out
+        shape = inp.get_shape().as_list()
+        _, h, w, c = shape
+        s = self.lay.stride
+        out = list()
+        for i in range(int(h/s)):
+            row_i = list()
+            for j in range(int(w/s)):
+                si, sj = s * i, s * j
+                boxij = inp[:, si: si+s, sj: sj+s,:]
+                flatij = tf.reshape(boxij, [-1,1,1,c*s*s])
+                row_i += [flatij]
+            out += [tf.concat(row_i, 2)]
+
+        self.out = tf.concat(out, 1)
+
+    def forward(self):
+        inp = self.inp.out
+        s = self.lay.stride
+        self.out = tf.extract_image_patches(
+            inp, [1,s,s,1], [1,s,s,1], [1,1,1,1], 'VALID')
+
+    def speak(self):
+        args = [self.lay.stride] * 2
+        msg = 'local flatten {}x{}'
+        return msg.format(*args)
+
+
+class local(BaseOp):
+    def forward(self):
+        pad = [[self.lay.pad, self.lay.pad]] * 2;
+        temp = tf.pad(self.inp.out, [[0, 0]] + pad + [[0, 0]])
+
+        k = self.lay.w['kernels']
+        ksz = self.lay.ksize
+        half = int(ksz / 2)
+        out = list()
+        for i in range(self.lay.h_out):
+            row_i = list()
+            for j in range(self.lay.w_out):
+                kij = k[i * self.lay.w_out + j]
+                i_, j_ = i + 1 - half, j + 1 - half
+                tij = temp[:, i_ : i_ + ksz, j_ : j_ + ksz,:]
+                row_i.append(
+                    tf.nn.conv2d(tij, kij, 
+                        padding = 'VALID', 
+                        strides = [1] * 4))
+            out += [tf.concat(row_i, 2)]
+
+        self.out = tf.concat(out, 1)
+
+    def speak(self):
+        l = self.lay
+        args = [l.ksize] * 2 + [l.pad] + [l.stride]
+        args += [l.activation]
+        msg = 'loca {}x{}p{}_{}  {}'.format(*args)
+        return msg
+
+class convolutional(BaseOp):
+    def forward(self):
+        pad = [[self.lay.pad, self.lay.pad]] * 2;
+        temp = tf.pad(self.inp.out, [[0, 0]] + pad + [[0, 0]])
+        temp = tf.nn.conv2d(temp, self.lay.w['kernel'], padding = 'VALID', 
+            name = self.scope, strides = [1] + [self.lay.stride] * 2 + [1])
+        if self.lay.batch_norm: 
+            temp = self.batchnorm(self.lay, temp)
+        self.out = tf.nn.bias_add(temp, self.lay.w['biases'])
+
+    def batchnorm(self, layer, inp):
+        if not self.var:
+            temp = (inp - layer.w['moving_mean'])
+            temp /= (np.sqrt(layer.w['moving_variance']) + 1e-5)
+            temp *= layer.w['gamma']
+            return temp
+        else:
+            args = dict({
+                'center' : False, 'scale' : True,
+                'epsilon': 1e-5, 'scope' : self.scope,
+                'updates_collections' : None,
+                'is_training': layer.h['is_training'],
+                'param_initializers': layer.w
+                })
+            return slim.batch_norm(inp, **args)
+
+    def speak(self):
+        l = self.lay
+        args = [l.ksize] * 2 + [l.pad] + [l.stride]
+        args += [l.batch_norm * '+bnorm']
+        args += [l.activation]
+        msg = 'conv {}x{}p{}_{}  {}  {}'.format(*args)
+        return msg
+
+class conv_select(convolutional):
+    def speak(self):
+        l = self.lay
+        args = [l.ksize] * 2 + [l.pad] + [l.stride]
+        args += [l.batch_norm * '+bnorm']
+        args += [l.activation]
+        msg = 'sele {}x{}p{}_{}  {}  {}'.format(*args)
+        return msg
+
+class conv_extract(convolutional):
+    def speak(self):
+        l = self.lay
+        args = [l.ksize] * 2 + [l.pad] + [l.stride]
+        args += [l.batch_norm * '+bnorm']
+        args += [l.activation]
+        msg = 'extr {}x{}p{}_{}  {}  {}'.format(*args)
+        return msg
\ No newline at end of file
diff --git a/darkflow/net/ops/simple.py b/darkflow/net/ops/simple.py
new file mode 100644
index 0000000..01e28ce
--- /dev/null
+++ b/darkflow/net/ops/simple.py
@@ -0,0 +1,133 @@
+import tensorflow.contrib.slim as slim
+from .baseop import BaseOp
+import tensorflow as tf
+from distutils.version import StrictVersion
+
+class route(BaseOp):
+	def forward(self):
+		routes = self.lay.routes
+		routes_out = list()
+		for r in routes:
+			this = self.inp
+			while this.lay.number != r:
+				this = this.inp
+				assert this is not None, \
+				'Routing to non-existence {}'.format(r)
+			routes_out += [this.out]
+		self.out = tf.concat(routes_out, 3)
+
+	def speak(self):
+		msg = 'concat {}'
+		return msg.format(self.lay.routes)
+
+class connected(BaseOp):
+	def forward(self):
+		self.out = tf.nn.xw_plus_b(
+			self.inp.out,
+			self.lay.w['weights'], 
+			self.lay.w['biases'], 
+			name = self.scope)
+
+	def speak(self):
+		layer = self.lay
+		args = [layer.inp, layer.out]
+		args += [layer.activation]
+		msg = 'full {} x {}  {}'
+		return msg.format(*args)
+
+class select(connected):
+	"""a weird connected layer"""
+	def speak(self):
+		layer = self.lay
+		args = [layer.inp, layer.out]
+		args += [layer.activation]
+		msg = 'sele {} x {}  {}'
+		return msg.format(*args)
+
+class extract(connected):
+	"""a weird connected layer"""
+	def speak(self):
+		layer = self.lay
+		args = [len(layer.inp), len(layer.out)]
+		args += [layer.activation]
+		msg = 'extr {} x {}  {}'
+		return msg.format(*args)
+
+class flatten(BaseOp):
+	def forward(self):
+		temp = tf.transpose(
+			self.inp.out, [0,3,1,2])
+		self.out = slim.flatten(
+			temp, scope = self.scope)
+
+	def speak(self): return 'flat'
+
+
+class softmax(BaseOp):
+	def forward(self):
+		self.out = tf.nn.softmax(self.inp.out)
+
+	def speak(self): return 'softmax()'
+
+
+class avgpool(BaseOp):
+	def forward(self):
+		self.out = tf.reduce_mean(
+			self.inp.out, [1, 2], 
+			name = self.scope
+		)
+
+	def speak(self): return 'avgpool()'
+
+
+class dropout(BaseOp):
+	def forward(self):
+		if self.lay.h['pdrop'] is None:
+			self.lay.h['pdrop'] = 1.0
+		self.out = tf.nn.dropout(
+			self.inp.out, 
+			self.lay.h['pdrop'], 
+			name = self.scope
+		)
+
+	def speak(self): return 'drop'
+
+
+class crop(BaseOp):
+	def forward(self):
+		self.out =  self.inp.out * 2. - 1.
+
+	def speak(self):
+		return 'scale to (-1, 1)'
+
+
+class maxpool(BaseOp):
+	def forward(self):
+		self.out = tf.nn.max_pool(
+			self.inp.out, padding = 'SAME',
+	        ksize = [1] + [self.lay.ksize]*2 + [1], 
+	        strides = [1] + [self.lay.stride]*2 + [1],
+	        name = self.scope
+	    )
+	
+	def speak(self):
+		l = self.lay
+		return 'maxp {}x{}p{}_{}'.format(
+			l.ksize, l.ksize, l.pad, l.stride)
+
+
+class leaky(BaseOp):
+	def forward(self):
+		self.out = tf.maximum(
+			.1 * self.inp.out, 
+			self.inp.out, 
+			name = self.scope
+		)
+
+	def verbalise(self): pass
+
+
+class identity(BaseOp):
+	def __init__(self, inp):
+		self.inp = None
+		self.out = inp
diff --git a/darkflow/net/vanilla/__init__.py b/darkflow/net/vanilla/__init__.py
new file mode 100644
index 0000000..b189372
--- /dev/null
+++ b/darkflow/net/vanilla/__init__.py
@@ -0,0 +1,4 @@
+from . import train
+
+def constructor(self, meta, FLAGS):
+	self.meta, self.FLAGS = meta, FLAGS
\ No newline at end of file
diff --git a/darkflow/net/vanilla/train.py b/darkflow/net/vanilla/train.py
new file mode 100644
index 0000000..1785d04
--- /dev/null
+++ b/darkflow/net/vanilla/train.py
@@ -0,0 +1,42 @@
+_LOSS_TYPE = ['sse','l2', 'smooth',
+			  'sparse', 'l1', 'softmax',
+			  'svm', 'fisher']
+
+def loss(self, net_out):
+	m = self.meta
+	loss_type = self.meta['type']
+	assert loss_type in _LOSS_TYPE, \
+	'Loss type {} not implemented'.format(loss_type)
+
+	out = net_out
+	out_shape = out.get_shape()
+	out_dtype = out.dtype.base_dtype
+	_truth = tf.placeholders(out_dtype, out_shape)
+
+	self.placeholders = dict({
+			'truth': _truth
+		})
+
+	diff = _truth - out
+	if loss_type in ['sse','12']:
+		loss = tf.nn.l2_loss(diff)
+
+	elif loss_type == ['smooth']:
+		small = tf.cast(diff < 1, tf.float32)
+		large = 1. - small
+		l1_loss = tf.nn.l1_loss(tf.multiply(diff, large))
+		l2_loss = tf.nn.l2_loss(tf.multiply(diff, small))
+		loss = l1_loss + l2_loss
+
+	elif loss_type in ['sparse', 'l1']:
+		loss = l1_loss(diff)
+
+	elif loss_type == 'softmax':
+		loss = tf.nn.softmax_cross_entropy_with_logits(logits, y)
+		loss = tf.reduce_mean(loss)
+
+	elif loss_type == 'svm':
+		assert 'train_size' in m, \
+		'Must specify'
+		size = m['train_size']
+		self.nu = tf.Variable(tf.ones([train_size, num_classes]))
\ No newline at end of file
diff --git a/darkflow/net/yolo/__init__.py b/darkflow/net/yolo/__init__.py
new file mode 100644
index 0000000..1ad7d54
--- /dev/null
+++ b/darkflow/net/yolo/__init__.py
@@ -0,0 +1,37 @@
+from . import train
+from . import predict
+from . import data
+from . import misc
+import numpy as np
+
+
+""" YOLO framework __init__ equivalent"""
+
+def constructor(self, meta, FLAGS):
+
+	def _to_color(indx, base):
+		""" return (b, r, g) tuple"""
+		base2 = base * base
+		b = 2 - indx / base2
+		r = 2 - (indx % base2) / base
+		g = 2 - (indx % base2) % base
+		return (b * 127, r * 127, g * 127)
+	if 'labels' not in meta:
+		misc.labels(meta, FLAGS) #We're not loading from a .pb so we do need to load the labels
+	assert len(meta['labels']) == meta['classes'], (
+		'labels.txt and {} indicate' + ' '
+		'inconsistent class numbers'
+	).format(meta['model'])
+
+	# assign a color for each label
+	colors = list()
+	base = int(np.ceil(pow(meta['classes'], 1./3)))
+	for x in range(len(meta['labels'])): 
+		colors += [_to_color(x, base)]
+	meta['colors'] = colors
+	self.fetch = list()
+	self.meta, self.FLAGS = meta, FLAGS
+
+	# over-ride the threshold in meta if FLAGS has it.
+	if FLAGS.threshold > 0.0:
+		self.meta['thresh'] = FLAGS.threshold
\ No newline at end of file
diff --git a/darkflow/net/yolo/data.py b/darkflow/net/yolo/data.py
new file mode 100644
index 0000000..f216e7f
--- /dev/null
+++ b/darkflow/net/yolo/data.py
@@ -0,0 +1,130 @@
+from ...utils.pascal_voc_clean_xml import pascal_voc_clean_xml
+from numpy.random import permutation as perm
+from .predict import preprocess
+# from .misc import show
+from copy import deepcopy
+import pickle
+import numpy as np
+import os 
+
+def parse(self, exclusive = False):
+    meta = self.meta
+    ext = '.parsed'
+    ann = self.FLAGS.annotation
+    if not os.path.isdir(ann):
+        msg = 'Annotation directory not found {} .'
+        exit('Error: {}'.format(msg.format(ann)))
+    print('\n{} parsing {}'.format(meta['model'], ann))
+    dumps = pascal_voc_clean_xml(ann, meta['labels'], exclusive)
+    return dumps
+
+
+def _batch(self, chunk):
+    """
+    Takes a chunk of parsed annotations
+    returns value for placeholders of net's 
+    input & loss layer correspond to this chunk
+    """
+    meta = self.meta
+    S, B = meta['side'], meta['num']
+    C, labels = meta['classes'], meta['labels']
+
+    # preprocess
+    jpg = chunk[0]; w, h, allobj_ = chunk[1]
+    allobj = deepcopy(allobj_)
+    path = os.path.join(self.FLAGS.dataset, jpg)
+    img = self.preprocess(path, allobj)
+
+    # Calculate regression target
+    cellx = 1. * w / S
+    celly = 1. * h / S
+    for obj in allobj:
+        centerx = .5*(obj[1]+obj[3]) #xmin, xmax
+        centery = .5*(obj[2]+obj[4]) #ymin, ymax
+        cx = centerx / cellx
+        cy = centery / celly
+        if cx >= S or cy >= S: return None, None
+        obj[3] = float(obj[3]-obj[1]) / w
+        obj[4] = float(obj[4]-obj[2]) / h
+        obj[3] = np.sqrt(obj[3])
+        obj[4] = np.sqrt(obj[4])
+        obj[1] = cx - np.floor(cx) # centerx
+        obj[2] = cy - np.floor(cy) # centery
+        obj += [int(np.floor(cy) * S + np.floor(cx))]
+
+    # show(im, allobj, S, w, h, cellx, celly) # unit test
+
+    # Calculate placeholders' values
+    probs = np.zeros([S*S,C])
+    confs = np.zeros([S*S,B])
+    coord = np.zeros([S*S,B,4])
+    proid = np.zeros([S*S,C])
+    prear = np.zeros([S*S,4])
+    for obj in allobj:
+        probs[obj[5], :] = [0.] * C
+        probs[obj[5], labels.index(obj[0])] = 1.
+        proid[obj[5], :] = [1] * C
+        coord[obj[5], :, :] = [obj[1:5]] * B
+        prear[obj[5],0] = obj[1] - obj[3]**2 * .5 * S # xleft
+        prear[obj[5],1] = obj[2] - obj[4]**2 * .5 * S # yup
+        prear[obj[5],2] = obj[1] + obj[3]**2 * .5 * S # xright
+        prear[obj[5],3] = obj[2] + obj[4]**2 * .5 * S # ybot
+        confs[obj[5], :] = [1.] * B
+
+    # Finalise the placeholders' values
+    upleft   = np.expand_dims(prear[:,0:2], 1)
+    botright = np.expand_dims(prear[:,2:4], 1)
+    wh = botright - upleft; 
+    area = wh[:,:,0] * wh[:,:,1]
+    upleft   = np.concatenate([upleft] * B, 1)
+    botright = np.concatenate([botright] * B, 1)
+    areas = np.concatenate([area] * B, 1)
+
+    # value for placeholder at input layer
+    inp_feed_val = img
+    # value for placeholder at loss layer 
+    loss_feed_val = {
+        'probs': probs, 'confs': confs, 
+        'coord': coord, 'proid': proid,
+        'areas': areas, 'upleft': upleft, 
+        'botright': botright
+    }
+
+    return inp_feed_val, loss_feed_val
+
+def shuffle(self):
+    batch = self.FLAGS.batch
+    data = self.parse()
+    size = len(data)
+
+    print('Dataset of {} instance(s)'.format(size))
+    if batch > size: self.FLAGS.batch = batch = size
+    batch_per_epoch = int(size / batch)
+
+    for i in range(self.FLAGS.epoch):
+        shuffle_idx = perm(np.arange(size))
+        for b in range(batch_per_epoch):
+            # yield these
+            x_batch = list()
+            feed_batch = dict()
+
+            for j in range(b*batch, b*batch+batch):
+                train_instance = data[shuffle_idx[j]]
+                inp, new_feed = self._batch(train_instance)
+
+                if inp is None: continue
+                x_batch += [np.expand_dims(inp, 0)]
+
+                for key in new_feed:
+                    new = new_feed[key]
+                    old_feed = feed_batch.get(key, 
+                        np.zeros((0,) + new.shape))
+                    feed_batch[key] = np.concatenate([ 
+                        old_feed, [new] 
+                    ])      
+            
+            x_batch = np.concatenate(x_batch, 0)
+            yield x_batch, feed_batch
+        
+        print('Finish {} epoch(es)'.format(i + 1))
+
diff --git a/darkflow/net/yolo/misc.py b/darkflow/net/yolo/misc.py
new file mode 100644
index 0000000..c7aeae4
--- /dev/null
+++ b/darkflow/net/yolo/misc.py
@@ -0,0 +1,130 @@
+import pickle
+import numpy as np
+import cv2
+import os
+
+labels20 = ["aeroplane", "bicycle", "bird", "boat", "bottle",
+    "bus", "car", "cat", "chair", "cow", "diningtable", "dog",
+    "horse", "motorbike", "person", "pottedplant", "sheep", "sofa",
+    "train", "tvmonitor"]
+
+# 8, 14, 15, 19
+
+voc_models = ['yolo-full', 'yolo-tiny', 'yolo-small',  # <- v1
+              'yolov1', 'tiny-yolov1', # <- v1.1 
+              'tiny-yolo-voc', 'yolo-voc'] # <- v2
+
+coco_models = ['tiny-coco', 'yolo-coco',  # <- v1.1
+               'yolo', 'tiny-yolo'] # <- v2
+
+coco_names = 'coco.names'
+nine_names = '9k.names'
+
+def labels(meta, FLAGS):    
+    model = os.path.basename(meta['name'])
+    if model in voc_models: 
+        print("Model has a VOC model name, loading VOC labels.")
+        meta['labels'] = labels20
+    else:
+        file = FLAGS.labels
+        if model in coco_models:
+            print("Model has a coco model name, loading coco labels.")
+            file = os.path.join(FLAGS.config, coco_names)
+        elif model == 'yolo9000':
+            print("Model has name yolo9000, loading yolo9000 labels.")
+            file = os.path.join(FLAGS.config, nine_names)
+        with open(file, 'r') as f:
+            meta['labels'] = list()
+            labs = [l.strip() for l in f.readlines()]
+            for lab in labs:
+                if lab == '----': break
+                meta['labels'] += [lab]
+    if len(meta['labels']) == 0: 
+        meta['labels'] = labels20
+
+def is_inp(self, name): 
+    return name[-4:] in ['.jpg','.JPG', '.jpeg', '.JPEG', '.png', '.PNG']
+
+def show(im, allobj, S, w, h, cellx, celly):
+    for obj in allobj:
+        a = obj[5] % S
+        b = obj[5] // S
+        cx = a + obj[1]
+        cy = b + obj[2]
+        centerx = cx * cellx
+        centery = cy * celly
+        ww = obj[3]**2 * w
+        hh = obj[4]**2 * h
+        cv2.rectangle(im,
+            (int(centerx - ww/2), int(centery - hh/2)),
+            (int(centerx + ww/2), int(centery + hh/2)),
+            (0,0,255), 2)
+    cv2.imshow("result", im)
+    cv2.waitKey()
+    cv2.destroyAllWindows()
+
+def show2(im, allobj):
+    for obj in allobj:
+        cv2.rectangle(im,
+            (obj[1], obj[2]), 
+            (obj[3], obj[4]), 
+            (0,0,255),2)
+    cv2.imshow('result', im)
+    cv2.waitKey()
+    cv2.destroyAllWindows()
+
+
+_MVA = .05
+
+def profile(self, net):
+    pass
+#     data = self.parse(exclusive = True)
+#     size = len(data); batch = self.FLAGS.batch
+#     all_inp_ = [x[0] for x in data]
+#     net.say('Will cycle through {} examples {} times'.format(
+#         len(all_inp_), net.FLAGS.epoch))
+
+#     fetch = list(); mvave = list(); names = list();
+#     this = net.top
+#     conv_lay = ['convolutional', 'connected', 'local', 'conv-select']
+#     while this.inp is not None:
+#         if this.lay.type in conv_lay:
+#             fetch = [this.out] + fetch
+#             names = [this.lay.signature] + names
+#             mvave = [None] + mvave 
+#         this = this.inp
+#     print(names)
+
+#     total = int(); allofthem = len(all_inp_) * net.FLAGS.epoch
+#     batch = min(net.FLAGS.batch, len(all_inp_))
+#     for count in range(net.FLAGS.epoch):
+#         net.say('EPOCH {}'.format(count))
+#         for j in range(len(all_inp_)/batch):
+#             inp_feed = list(); new_all = list()
+#             all_inp = all_inp_[j*batch: (j*batch+batch)]
+#             for inp in all_inp:
+#                 new_all += [inp]
+#                 this_inp = os.path.join(net.FLAGS.dataset, inp)
+#                 this_inp = net.framework.preprocess(this_inp)
+#                 expanded = np.expand_dims(this_inp, 0)
+#                 inp_feed.append(expanded)
+#             all_inp = new_all
+#             feed_dict = {net.inp : np.concatenate(inp_feed, 0)}
+#             out = net.sess.run(fetch, feed_dict)
+
+#             for i, o in enumerate(out):
+#                 oi = out[i];
+#                 dim = len(oi.shape) - 1
+#                 ai = mvave[i]; 
+#                 mi = np.mean(oi, tuple(range(dim)))
+#                 vi = np.var(oi, tuple(range(dim)))
+#                 if ai is None: mvave[i] = [mi, vi]
+#                 elif 'banana ninja yada yada':
+#                     ai[0] = (1 - _MVA) * ai[0] + _MVA * mi
+#                     ai[1] = (1 - _MVA) * ai[1] + _MVA * vi
+#             total += len(inp_feed)
+#             net.say('{} / {} = {}%'.format(
+#                 total, allofthem, 100. * total / allofthem))
+
+#         with open('profile', 'wb') as f:
+#             pickle.dump([mvave], f, protocol = -1)
diff --git a/darkflow/net/yolo/predict.py b/darkflow/net/yolo/predict.py
new file mode 100644
index 0000000..de3f789
--- /dev/null
+++ b/darkflow/net/yolo/predict.py
@@ -0,0 +1,123 @@
+from ...utils.im_transform import imcv2_recolor, imcv2_affine_trans
+from ...utils.box import BoundBox, box_iou, prob_compare
+import numpy as np
+import cv2
+import os
+import json
+from ...cython_utils.cy_yolo_findboxes import yolo_box_constructor
+
+def _fix(obj, dims, scale, offs):
+	for i in range(1, 5):
+		dim = dims[(i + 1) % 2]
+		off = offs[(i + 1) % 2]
+		obj[i] = int(obj[i] * scale - off)
+		obj[i] = max(min(obj[i], dim), 0)
+
+def resize_input(self, im):
+	h, w, c = self.meta['inp_size']
+	imsz = cv2.resize(im, (w, h))
+	imsz = imsz / 255.
+	imsz = imsz[:,:,::-1]
+	return imsz
+
+def process_box(self, b, h, w, threshold):
+	max_indx = np.argmax(b.probs)
+	max_prob = b.probs[max_indx]
+	label = self.meta['labels'][max_indx]
+	if max_prob > threshold:
+		left  = int ((b.x - b.w/2.) * w)
+		right = int ((b.x + b.w/2.) * w)
+		top   = int ((b.y - b.h/2.) * h)
+		bot   = int ((b.y + b.h/2.) * h)
+		if left  < 0    :  left = 0
+		if right > w - 1: right = w - 1
+		if top   < 0    :   top = 0
+		if bot   > h - 1:   bot = h - 1
+		mess = '{}'.format(label)
+		return (left, right, top, bot, mess, max_indx, max_prob)
+	return None
+
+def findboxes(self, net_out):
+	meta, FLAGS = self.meta, self.FLAGS
+	threshold = FLAGS.threshold
+	
+	boxes = []
+	boxes = yolo_box_constructor(meta, net_out, threshold)
+	
+	return boxes
+
+def preprocess(self, im, allobj = None):
+	"""
+	Takes an image, return it as a numpy tensor that is readily
+	to be fed into tfnet. If there is an accompanied annotation (allobj),
+	meaning this preprocessing is serving the train process, then this
+	image will be transformed with random noise to augment training data,
+	using scale, translation, flipping and recolor. The accompanied
+	parsed annotation (allobj) will also be modified accordingly.
+	"""
+	if type(im) is not np.ndarray:
+		im = cv2.imread(im)
+
+	if allobj is not None: # in training mode
+		result = imcv2_affine_trans(im)
+		im, dims, trans_param = result
+		scale, offs, flip = trans_param
+		for obj in allobj:
+			_fix(obj, dims, scale, offs)
+			if not flip: continue
+			obj_1_ =  obj[1]
+			obj[1] = dims[0] - obj[3]
+			obj[3] = dims[0] - obj_1_
+		im = imcv2_recolor(im)
+
+	im = self.resize_input(im)
+	if allobj is None: return im
+	return im#, np.array(im) # for unit testing
+
+def postprocess(self, net_out, im, save = True):
+	"""
+	Takes net output, draw predictions, save to disk
+	"""
+	meta, FLAGS = self.meta, self.FLAGS
+	threshold = FLAGS.threshold
+	colors, labels = meta['colors'], meta['labels']
+
+	boxes = self.findboxes(net_out)
+
+	if type(im) is not np.ndarray:
+		imgcv = cv2.imread(im)
+	else: imgcv = im
+
+	h, w, _ = imgcv.shape
+	resultsForJSON = []
+	for b in boxes:
+		boxResults = self.process_box(b, h, w, threshold)
+		if boxResults is None:
+			continue
+		left, right, top, bot, mess, max_indx, confidence = boxResults
+		thick = int((h + w) // 300)
+		if self.FLAGS.json:
+			resultsForJSON.append({"label": mess, "confidence": float('%.2f' % confidence), "topleft": {"x": left, "y": top}, "bottomright": {"x": right, "y": bot}})
+			continue
+
+		cv2.rectangle(imgcv,
+			(left, top), (right, bot),
+			self.meta['colors'][max_indx], thick)
+		cv2.putText(
+			imgcv, mess, (left, top - 12),
+			0, 1e-3 * h, self.meta['colors'][max_indx],
+			thick // 3)
+
+
+	if not save: return imgcv
+
+	outfolder = os.path.join(self.FLAGS.imgdir, 'out')
+	img_name = os.path.join(outfolder, os.path.basename(im))
+	if self.FLAGS.json:
+		textJSON = json.dumps(resultsForJSON)
+		textFile = os.path.splitext(img_name)[0] + ".json"
+		with open(textFile, 'w') as f:
+			f.write(textJSON)
+		return	
+
+	cv2.imwrite(img_name, imgcv)
diff --git a/darkflow/net/yolo/train.py b/darkflow/net/yolo/train.py
new file mode 100644
index 0000000..78d8a2f
--- /dev/null
+++ b/darkflow/net/yolo/train.py
@@ -0,0 +1,92 @@
+import tensorflow.contrib.slim as slim
+import pickle
+import tensorflow as tf
+from .misc import show
+import numpy as np
+import os
+
+def loss(self, net_out):
+    """
+    Takes net.out and placeholders value
+    returned in batch() func above,
+    to build train_op and loss
+    """
+    # meta
+    m = self.meta
+    sprob = float(m['class_scale'])
+    sconf = float(m['object_scale'])
+    snoob = float(m['noobject_scale'])
+    scoor = float(m['coord_scale'])
+    S, B, C = m['side'], m['num'], m['classes']
+    SS = S * S # number of grid cells
+
+    print('{} loss hyper-parameters:'.format(m['model']))
+    print('\tside    = {}'.format(m['side']))
+    print('\tbox     = {}'.format(m['num']))
+    print('\tclasses = {}'.format(m['classes']))
+    print('\tscales  = {}'.format([sprob, sconf, snoob, scoor]))
+
+    size1 = [None, SS, C]
+    size2 = [None, SS, B]
+
+    # return the below placeholders
+    _probs = tf.placeholder(tf.float32, size1)
+    _confs = tf.placeholder(tf.float32, size2)
+    _coord = tf.placeholder(tf.float32, size2 + [4])
+    # weights term for L2 loss
+    _proid = tf.placeholder(tf.float32, size1)
+    # material calculating IOU
+    _areas = tf.placeholder(tf.float32, size2)
+    _upleft = tf.placeholder(tf.float32, size2 + [2])
+    _botright = tf.placeholder(tf.float32, size2 + [2])
+
+    self.placeholders = {
+        'probs':_probs, 'confs':_confs, 'coord':_coord, 'proid':_proid,
+        'areas':_areas, 'upleft':_upleft, 'botright':_botright
+    }
+
+    # Extract the coordinate prediction from net.out
+    coords = net_out[:, SS * (C + B):]
+    coords = tf.reshape(coords, [-1, SS, B, 4])
+    wh = tf.pow(coords[:,:,:,2:4], 2) * S # unit: grid cell
+    area_pred = wh[:,:,:,0] * wh[:,:,:,1] # unit: grid cell^2
+    centers = coords[:,:,:,0:2] # [batch, SS, B, 2]
+    floor = centers - (wh * .5) # [batch, SS, B, 2]
+    ceil  = centers + (wh * .5) # [batch, SS, B, 2]
+
+    # calculate the intersection areas
+    intersect_upleft   = tf.maximum(floor, _upleft)
+    intersect_botright = tf.minimum(ceil , _botright)
+    intersect_wh = intersect_botright - intersect_upleft
+    intersect_wh = tf.maximum(intersect_wh, 0.0)
+    intersect = tf.multiply(intersect_wh[:,:,:,0], intersect_wh[:,:,:,1])
+
+    # calculate the best IOU, set 0.0 confidence for worse boxes
+    iou = tf.truediv(intersect, _areas + area_pred - intersect)
+    best_box = tf.equal(iou, tf.reduce_max(iou, [2], True))
+    best_box = tf.to_float(best_box)
+    confs = tf.multiply(best_box, _confs)
+
+    # take care of the weight terms
+    conid = snoob * (1. - confs) + sconf * confs
+    weight_coo = tf.concat(4 * [tf.expand_dims(confs, -1)], 3)
+    cooid = scoor * weight_coo
+    proid = sprob * _proid
+
+    # flatten 'em all
+    probs = slim.flatten(_probs)
+    proid = slim.flatten(proid)
+    confs = slim.flatten(confs)
+    conid = slim.flatten(conid)
+    coord = slim.flatten(_coord)
+    cooid = slim.flatten(cooid)
+
+    self.fetch += [probs, confs, conid, cooid, proid]
+    true = tf.concat([probs, confs, coord], 1)
+    wght = tf.concat([proid, conid, cooid], 1)
+    print('Building {} loss'.format(m['model']))
+    loss = tf.pow(net_out - true, 2)
+    loss = tf.multiply(loss, wght)
+    loss = tf.reduce_sum(loss, 1)
+    self.loss = .5 * tf.reduce_mean(loss)
+    tf.summary.scalar('{} loss'.format(m['model']), self.loss)
diff --git a/darkflow/net/yolov2/__init__.py b/darkflow/net/yolov2/__init__.py
new file mode 100644
index 0000000..41f89e5
--- /dev/null
+++ b/darkflow/net/yolov2/__init__.py
@@ -0,0 +1,5 @@
+from . import train
+from . import predict
+from . import data
+from ..yolo import misc
+import numpy as np
diff --git a/darkflow/net/yolov2/data.py b/darkflow/net/yolov2/data.py
new file mode 100644
index 0000000..fbfcdfc
--- /dev/null
+++ b/darkflow/net/yolov2/data.py
@@ -0,0 +1,85 @@
+from ...utils.pascal_voc_clean_xml import pascal_voc_clean_xml
+from numpy.random import permutation as perm
+from ..yolo.predict import preprocess
+from ..yolo.data import shuffle
+from copy import deepcopy
+import pickle
+import numpy as np
+import os
+
+def _batch(self, chunk):
+    """
+    Takes a chunk of parsed annotations
+    returns value for placeholders of net's 
+    input & loss layer correspond to this chunk
+    """
+    meta = self.meta
+    labels = meta['labels']
+    
+    H, W, _ = meta['out_size']
+    C, B = meta['classes'], meta['num']
+    anchors = meta['anchors']
+
+    # preprocess
+    jpg = chunk[0]; w, h, allobj_ = chunk[1]
+    allobj = deepcopy(allobj_)
+    path = os.path.join(self.FLAGS.dataset, jpg)
+    img = self.preprocess(path, allobj)
+
+    # Calculate regression target
+    cellx = 1. * w / W
+    celly = 1. * h / H
+    for obj in allobj:
+        centerx = .5*(obj[1]+obj[3]) #xmin, xmax
+        centery = .5*(obj[2]+obj[4]) #ymin, ymax
+        cx = centerx / cellx
+        cy = centery / celly
+        if cx >= W or cy >= H: return None, None
+        obj[3] = float(obj[3]-obj[1]) / w
+        obj[4] = float(obj[4]-obj[2]) / h
+        obj[3] = np.sqrt(obj[3])
+        obj[4] = np.sqrt(obj[4])
+        obj[1] = cx - np.floor(cx) # centerx
+        obj[2] = cy - np.floor(cy) # centery
+        obj += [int(np.floor(cy) * W + np.floor(cx))]
+
+    # show(im, allobj, S, w, h, cellx, celly) # unit test
+
+    # Calculate placeholders' values
+    probs = np.zeros([H*W,B,C])
+    confs = np.zeros([H*W,B])
+    coord = np.zeros([H*W,B,4])
+    proid = np.zeros([H*W,B,C])
+    prear = np.zeros([H*W,4])
+    for obj in allobj:
+        probs[obj[5], :, :] = [[0.]*C] * B
+        probs[obj[5], :, labels.index(obj[0])] = 1.
+        proid[obj[5], :, :] = [[1.]*C] * B
+        coord[obj[5], :, :] = [obj[1:5]] * B
+        prear[obj[5],0] = obj[1] - obj[3]**2 * .5 * W # xleft
+        prear[obj[5],1] = obj[2] - obj[4]**2 * .5 * H # yup
+        prear[obj[5],2] = obj[1] + obj[3]**2 * .5 * W # xright
+        prear[obj[5],3] = obj[2] + obj[4]**2 * .5 * H # ybot
+        confs[obj[5], :] = [1.] * B
+
+    # Finalise the placeholders' values
+    upleft   = np.expand_dims(prear[:,0:2], 1)
+    botright = np.expand_dims(prear[:,2:4], 1)
+    wh = botright - upleft; 
+    area = wh[:,:,0] * wh[:,:,1]
+    upleft   = np.concatenate([upleft] * B, 1)
+    botright = np.concatenate([botright] * B, 1)
+    areas = np.concatenate([area] * B, 1)
+
+    # value for placeholder at input layer
+    inp_feed_val = img
+    # value for placeholder at loss layer 
+    loss_feed_val = {
+        'probs': probs, 'confs': confs, 
+        'coord': coord, 'proid': proid,
+        'areas': areas, 'upleft': upleft, 
+        'botright': botright
+    }
+
+    return inp_feed_val, loss_feed_val
+
diff --git a/darkflow/net/yolov2/predict.py b/darkflow/net/yolov2/predict.py
new file mode 100644
index 0000000..b3485c4
--- /dev/null
+++ b/darkflow/net/yolov2/predict.py
@@ -0,0 +1,71 @@
+import numpy as np
+import math
+import cv2
+import os
+import json
+#from scipy.special import expit
+#from utils.box import BoundBox, box_iou, prob_compare
+#from utils.box import prob_compare2, box_intersection
+from ...utils.box import BoundBox
+from ...cython_utils.cy_yolo2_findboxes import box_constructor
+
+def expit(x):
+	return 1. / (1. + np.exp(-x))
+
+def _softmax(x):
+    e_x = np.exp(x - np.max(x))
+    out = e_x / e_x.sum()
+    return out
+
+def findboxes(self, net_out):
+	# meta
+	meta = self.meta
+	boxes = list()
+	boxes=box_constructor(meta,net_out)
+	return boxes
+
+def postprocess(self, net_out, im, save = True):
+	"""
+	Takes net output, draw net_out, save to disk
+	"""
+	boxes = self.findboxes(net_out)
+
+	# meta
+	meta = self.meta
+	threshold = meta['thresh']
+	colors = meta['colors']
+	labels = meta['labels']
+	if type(im) is not np.ndarray:
+		imgcv = cv2.imread(im)
+	else: imgcv = im
+	h, w, _ = imgcv.shape
+	
+	resultsForJSON = []
+	for b in boxes:
+		boxResults = self.process_box(b, h, w, threshold)
+		if boxResults is None:
+			continue
+		left, right, top, bot, mess, max_indx, confidence = boxResults
+		thick = int((h + w) // 300)
+		if self.FLAGS.json:
+			resultsForJSON.append({"label": mess, "confidence": float('%.2f' % confidence), "topleft": {"x": left, "y": top}, "bottomright": {"x": right, "y": bot}})
+			continue
+
+		cv2.rectangle(imgcv,
+			(left, top), (right, bot),
+			colors[max_indx], thick)
+		cv2.putText(imgcv, mess, (left, top - 12),
+			0, 1e-3 * h, colors[max_indx],thick//3)
+
+	if not save: return imgcv
+
+	outfolder = os.path.join(self.FLAGS.imgdir, 'out')
+	img_name = os.path.join(outfolder, os.path.basename(im))
+	if self.FLAGS.json:
+		textJSON = json.dumps(resultsForJSON)
+		textFile = os.path.splitext(img_name)[0] + ".json"
+		with open(textFile, 'w') as f:
+			f.write(textJSON)
+		return
+
+	cv2.imwrite(img_name, imgcv)
diff --git a/darkflow/net/yolov2/train.py b/darkflow/net/yolov2/train.py
new file mode 100644
index 0000000..a0bf715
--- /dev/null
+++ b/darkflow/net/yolov2/train.py
@@ -0,0 +1,107 @@
+import tensorflow.contrib.slim as slim
+import pickle
+import tensorflow as tf
+from ..yolo.misc import show
+import numpy as np
+import os
+import math
+
+def expit_tensor(x):
+	return 1. / (1. + tf.exp(-x))
+
+def loss(self, net_out):
+    """
+    Takes net.out and placeholders value
+    returned in batch() func above,
+    to build train_op and loss
+    """
+    # meta
+    m = self.meta
+    sprob = float(m['class_scale'])
+    sconf = float(m['object_scale'])
+    snoob = float(m['noobject_scale'])
+    scoor = float(m['coord_scale'])
+    H, W, _ = m['out_size']
+    B, C = m['num'], m['classes']
+    HW = H * W # number of grid cells
+    anchors = m['anchors']
+
+    print('{} loss hyper-parameters:'.format(m['model']))
+    print('\tH       = {}'.format(H))
+    print('\tW       = {}'.format(W))
+    print('\tbox     = {}'.format(m['num']))
+    print('\tclasses = {}'.format(m['classes']))
+    print('\tscales  = {}'.format([sprob, sconf, snoob, scoor]))
+
+    size1 = [None, HW, B, C]
+    size2 = [None, HW, B]
+
+    # return the below placeholders
+    _probs = tf.placeholder(tf.float32, size1)
+    _confs = tf.placeholder(tf.float32, size2)
+    _coord = tf.placeholder(tf.float32, size2 + [4])
+    # weights term for L2 loss
+    _proid = tf.placeholder(tf.float32, size1)
+    # material calculating IOU
+    _areas = tf.placeholder(tf.float32, size2)
+    _upleft = tf.placeholder(tf.float32, size2 + [2])
+    _botright = tf.placeholder(tf.float32, size2 + [2])
+
+    self.placeholders = {
+        'probs':_probs, 'confs':_confs, 'coord':_coord, 'proid':_proid,
+        'areas':_areas, 'upleft':_upleft, 'botright':_botright
+    }
+
+    # Extract the coordinate prediction from net.out
+    net_out_reshape = tf.reshape(net_out, [-1, H, W, B, (4 + 1 + C)])
+    coords = net_out_reshape[:, :, :, :, :4]
+    coords = tf.reshape(coords, [-1, H*W, B, 4])
+    adjusted_coords_xy = expit_tensor(coords[:,:,:,0:2])
+    adjusted_coords_wh = tf.sqrt(tf.exp(coords[:,:,:,2:4]) * np.reshape(anchors, [1, 1, B, 2]) / np.reshape([W, H], [1, 1, 1, 2]))
+    coords = tf.concat([adjusted_coords_xy, adjusted_coords_wh], 3)
+
+    adjusted_c = expit_tensor(net_out_reshape[:, :, :, :, 4])
+    adjusted_c = tf.reshape(adjusted_c, [-1, H*W, B, 1])
+
+    adjusted_prob = tf.nn.softmax(net_out_reshape[:, :, :, :, 5:])
+    adjusted_prob = tf.reshape(adjusted_prob, [-1, H*W, B, C])
+
+    adjusted_net_out = tf.concat([adjusted_coords_xy, adjusted_coords_wh, adjusted_c, adjusted_prob], 3)
+
+    wh = tf.pow(coords[:,:,:,2:4], 2) * np.reshape([W, H], [1, 1, 1, 2])
+    area_pred = wh[:,:,:,0] * wh[:,:,:,1]
+    centers = coords[:,:,:,0:2]
+    floor = centers - (wh * .5)
+    ceil  = centers + (wh * .5)
+
+    # calculate the intersection areas
+    intersect_upleft   = tf.maximum(floor, _upleft)
+    intersect_botright = tf.minimum(ceil , _botright)
+    intersect_wh = intersect_botright - intersect_upleft
+    intersect_wh = tf.maximum(intersect_wh, 0.0)
+    intersect = tf.multiply(intersect_wh[:,:,:,0], intersect_wh[:,:,:,1])
+
+    # calculate the best IOU, set 0.0 confidence for worse boxes
+    iou = tf.truediv(intersect, _areas + area_pred - intersect)
+    best_box = tf.equal(iou, tf.reduce_max(iou, [2], True))
+    best_box = tf.to_float(best_box)
+    confs = tf.multiply(best_box, _confs)
+
+    # take care of the weight terms
+    conid = snoob * (1. - confs) + sconf * confs
+    weight_coo = tf.concat(4 * [tf.expand_dims(confs, -1)], 3)
+    cooid = scoor * weight_coo
+    weight_pro = tf.concat(C * [tf.expand_dims(confs, -1)], 3)
+    proid = sprob * weight_pro
+
+    self.fetch += [_probs, confs, conid, cooid, proid]
+    true = tf.concat([_coord, tf.expand_dims(confs, 3), _probs ], 3)
+    wght = tf.concat([cooid, tf.expand_dims(conid, 3), proid ], 3)
+
+    print('Building {} loss'.format(m['model']))
+    loss = tf.pow(adjusted_net_out - true, 2)
+    loss = tf.multiply(loss, wght)
+    loss = tf.reshape(loss, [-1, H*W*B*(4 + 1 + C)])
+    loss = tf.reduce_sum(loss, 1)
+    self.loss = .5 * tf.reduce_mean(loss)
+    tf.summary.scalar('{} loss'.format(m['model']), self.loss)
\ No newline at end of file
diff --git a/darkflow/utils/box.py b/darkflow/utils/box.py
new file mode 100644
index 0000000..5136647
--- /dev/null
+++ b/darkflow/utils/box.py
@@ -0,0 +1,44 @@
+import numpy as np
+
+class BoundBox:
+    def __init__(self, classes):
+        self.x, self.y = float(), float()
+        self.w, self.h = float(), float()
+        self.c = float()
+        self.class_num = classes
+        self.probs = np.zeros((classes,))
+
+def overlap(x1,w1,x2,w2):
+    l1 = x1 - w1 / 2.;
+    l2 = x2 - w2 / 2.;
+    left = max(l1, l2)
+    r1 = x1 + w1 / 2.;
+    r2 = x2 + w2 / 2.;
+    right = min(r1, r2)
+    return right - left;
+
+def box_intersection(a, b):
+    w = overlap(a.x, a.w, b.x, b.w);
+    h = overlap(a.y, a.h, b.y, b.h);
+    if w < 0 or h < 0: return 0;
+    area = w * h;
+    return area;
+
+def box_union(a, b):
+    i = box_intersection(a, b);
+    u = a.w * a.h + b.w * b.h - i;
+    return u;
+
+def box_iou(a, b):
+    return box_intersection(a, b) / box_union(a, b);
+
+def prob_compare(box):
+    return box.probs[box.class_num]
+
+def prob_compare2(boxa, boxb):
+    if (boxa.pi < boxb.pi):
+        return 1
+    elif(boxa.pi == boxb.pi):
+        return 0
+    else:
+        return -1
\ No newline at end of file
diff --git a/darkflow/utils/im_transform.py b/darkflow/utils/im_transform.py
new file mode 100644
index 0000000..249e27f
--- /dev/null
+++ b/darkflow/utils/im_transform.py
@@ -0,0 +1,31 @@
+import numpy as np
+import cv2
+
+def imcv2_recolor(im, a = .1):
+	t = [np.random.uniform()]
+	t += [np.random.uniform()]
+	t += [np.random.uniform()]
+	t = np.array(t) * 2. - 1.
+
+	# random amplify each channel
+	im = im * (1 + t * a)
+	mx = 255. * (1 + a)
+	up = np.random.uniform() * 2 - 1
+# 	im = np.power(im/mx, 1. + up * .5)
+	im = cv2.pow(im/mx, 1. + up * .5)
+	return np.array(im * 255., np.uint8)
+
+def imcv2_affine_trans(im):
+	# Scale and translate
+	h, w, c = im.shape
+	scale = np.random.uniform() / 10. + 1.
+	max_offx = (scale-1.) * w
+	max_offy = (scale-1.) * h
+	offx = int(np.random.uniform() * max_offx)
+	offy = int(np.random.uniform() * max_offy)
+	
+	im = cv2.resize(im, (0,0), fx = scale, fy = scale)
+	im = im[offy : (offy + h), offx : (offx + w)]
+	flip = np.random.binomial(1, .5)
+	if flip: im = cv2.flip(im, 1)
+	return im, [w, h, c], [scale, [offx, offy], flip]
diff --git a/darkflow/utils/loader.py b/darkflow/utils/loader.py
new file mode 100644
index 0000000..723560d
--- /dev/null
+++ b/darkflow/utils/loader.py
@@ -0,0 +1,152 @@
+import tensorflow as tf
+import os
+from .. import dark
+import numpy as np
+from os.path import basename
+
+class loader(object):
+    """
+    interface to work with both .weights and .ckpt files
+    in loading / recollecting / resolving mode
+    """
+    VAR_LAYER = ['convolutional', 'connected', 'local', 
+                 'select', 'conv-select',
+                 'extract', 'conv-extract']
+
+    def __init__(self, *args):
+        self.src_key = list()
+        self.vals = list()
+        self.load(*args)
+
+    def __call__(self, key):
+        for idx in range(len(key)):
+            val = self.find(key, idx)
+            if val is not None: return val
+        return None
+    
+    def find(self, key, idx):
+        up_to = min(len(self.src_key), 4)
+        for i in range(up_to):
+            key_b = self.src_key[i]
+            if key_b[idx:] == key[idx:]:
+                return self.yields(i)
+        return None
+
+    def yields(self, idx):
+        del self.src_key[idx]
+        temp = self.vals[idx]
+        del self.vals[idx]
+        return temp
+
+class weights_loader(loader):
+    """one who understands .weights files"""
+    
+    _W_ORDER = dict({ # order of param flattened into .weights file
+        'convolutional': [
+            'biases','gamma','moving_mean','moving_variance','kernel'
+        ],
+        'connected': ['biases', 'weights'],
+        'local': ['biases', 'kernels']
+    })
+
+    def load(self, path, src_layers):
+        self.src_layers = src_layers
+        walker = weights_walker(path)
+
+        for i, layer in enumerate(src_layers):
+            if layer.type not in self.VAR_LAYER: continue
+            self.src_key.append([layer])
+            
+            if walker.eof: new = None
+            else: 
+                args = layer.signature
+                new = dark.darknet.create_darkop(*args)
+            self.vals.append(new)
+
+            if new is None: continue
+            order = self._W_ORDER[new.type]
+            for par in order:
+                if par not in new.wshape: continue
+                val = walker.walk(new.wsize[par])
+                new.w[par] = val
+            new.finalize(walker.transpose)
+
+        if walker.path is not None:
+            assert walker.offset == walker.size, \
+            'expect {} bytes, found {}'.format(
+                walker.offset, walker.size)
+            print('Successfully identified {} bytes'.format(
+                walker.offset))
+
+class checkpoint_loader(loader):
+    """
+    one who understands .ckpt files, very much
+    """
+    def load(self, ckpt, ignore):
+        meta = ckpt + '.meta'
+        with tf.Graph().as_default() as graph:
+            with tf.Session().as_default() as sess:
+                saver = tf.train.import_meta_graph(meta)
+                saver.restore(sess, ckpt)
+                for var in tf.global_variables():
+                    name = var.name.split(':')[0]
+                    packet = [name, var.get_shape().as_list()]
+                    self.src_key += [packet]
+                    self.vals += [var.eval(sess)]
+
+def create_loader(path, cfg = None):
+    if path is None:
+        load_type = weights_loader
+    elif '.weights' in path:
+        load_type = weights_loader
+    else: 
+        load_type = checkpoint_loader
+    
+    return load_type(path, cfg)
+
+class weights_walker(object):
+    """incremental reader of float32 binary files"""
+    def __init__(self, path):
+        self.eof = False # end of file
+        self.path = path  # current pos
+        if path is None: 
+            self.eof = True
+            return
+        else: 
+            self.size = os.path.getsize(path)# save the path
+            major, minor, revision, seen = np.memmap(path,
+                shape = (), mode = 'r', offset = 0,
+                dtype = '({})i4,'.format(4))
+            self.transpose = major > 1000 or minor > 1000
+            self.offset = 16
+
+    def walk(self, size):
+        if self.eof: return None
+        end_point = self.offset + 4 * size
+        assert end_point <= self.size, \
+        'Over-read {}'.format(self.path)
+
+        float32_1D_array = np.memmap(
+            self.path, shape = (), mode = 'r', 
+            offset = self.offset,
+            dtype='({})float32,'.format(size)
+        )
+
+        self.offset = end_point
+        if end_point == self.size: 
+            self.eof = True
+        return float32_1D_array
+
+def model_name(file_path):
+    file_name = basename(file_path)
+    ext = str()
+    if '.' in file_name: # exclude extension
+        file_name = file_name.split('.')
+        ext = file_name[-1]
+        file_name = '.'.join(file_name[:-1])
+    if ext == str() or ext == 'meta': # ckpt file
+        file_name = file_name.split('-')
+        num = int(file_name[-1])
+        return '-'.join(file_name[:-1])
+    if ext == 'weights':
+        return file_name
\ No newline at end of file
diff --git a/darkflow/utils/pascal_voc_clean_xml.py b/darkflow/utils/pascal_voc_clean_xml.py
new file mode 100644
index 0000000..70eb3e0
--- /dev/null
+++ b/darkflow/utils/pascal_voc_clean_xml.py
@@ -0,0 +1,79 @@
+"""
+parse PASCAL VOC xml annotations
+"""
+
+import os
+import sys
+import xml.etree.ElementTree as ET
+import glob
+
+
+def _pp(l): # pretty printing 
+    for i in l: print('{}: {}'.format(i,l[i]))
+
+def pascal_voc_clean_xml(ANN, pick, exclusive = False):
+    print('Parsing for {} {}'.format(
+            pick, 'exclusively' * int(exclusive)))
+
+    dumps = list()
+    cur_dir = os.getcwd()
+    os.chdir(ANN)
+    annotations = os.listdir('.')
+    annotations = glob.glob(str(annotations)+'*.xml')
+    size = len(annotations)
+
+    for i, file in enumerate(annotations):
+        # progress bar      
+        sys.stdout.write('\r')
+        percentage = 1. * (i+1) / size
+        progress = int(percentage * 20)
+        bar_arg = [progress*'=', ' '*(19-progress), percentage*100]
+        bar_arg += [file]
+        sys.stdout.write('[{}>{}]{:.0f}%  {}'.format(*bar_arg))
+        sys.stdout.flush()
+        
+        # actual parsing 
+        in_file = open(file)
+        tree=ET.parse(in_file)
+        root = tree.getroot()
+        jpg = str(root.find('filename').text)
+        imsize = root.find('size')
+        w = int(imsize.find('width').text)
+        h = int(imsize.find('height').text)
+        all = list()
+
+        for obj in root.iter('object'):
+                current = list()
+                name = obj.find('name').text
+                if name not in pick:
+                        continue
+
+                xmlbox = obj.find('bndbox')
+                xn = int(float(xmlbox.find('xmin').text))
+                xx = int(float(xmlbox.find('xmax').text))
+                yn = int(float(xmlbox.find('ymin').text))
+                yx = int(float(xmlbox.find('ymax').text))
+                current = [name,xn,yn,xx,yx]
+                all += [current]
+
+        add = [[jpg, [w, h, all]]]
+        dumps += add
+        in_file.close()
+
+    # gather all stats
+    stat = dict()
+    for dump in dumps:
+        all = dump[1][2]
+        for current in all:
+            if current[0] in pick:
+                if current[0] in stat:
+                    stat[current[0]]+=1
+                else:
+                    stat[current[0]] =1
+
+    print('\nStatistics:')
+    _pp(stat)
+    print('Dataset size: {}'.format(len(dumps)))
+
+    os.chdir(cur_dir)
+    return dumps
\ No newline at end of file
diff --git a/darkflow/utils/process.py b/darkflow/utils/process.py
new file mode 100644
index 0000000..0836f48
--- /dev/null
+++ b/darkflow/utils/process.py
@@ -0,0 +1,321 @@
+"""
+WARNING: spaghetti code.
+"""
+
+import numpy as np
+import pickle
+import os
+
+def parser(model):
+	"""
+	Read the .cfg file to extract layers into `layers`
+	as well as model-specific parameters into `meta`
+	"""
+	def _parse(l, i = 1):
+		return l.split('=')[i].strip()
+
+	with open(model, 'rb') as f:
+		lines = f.readlines()
+
+	lines = [line.decode() for line in lines]	
+	
+	meta = dict(); layers = list() # will contains layers' info
+	h, w, c = [int()] * 3; layer = dict()
+	for line in lines:
+		line = line.strip()
+		line = line.split('#')[0]
+		if '[' in line:
+			if layer != dict(): 
+				if layer['type'] == '[net]': 
+					h = layer['height']
+					w = layer['width']
+					c = layer['channels']
+					meta['net'] = layer
+				else:
+					if layer['type'] == '[crop]':
+						h = layer['crop_height']
+						w = layer['crop_width']
+					layers += [layer]				
+			layer = {'type': line}
+		else:
+			try:
+				i = float(_parse(line))
+				if i == int(i): i = int(i)
+				layer[line.split('=')[0].strip()] = i
+			except:
+				try:
+					key = _parse(line, 0)
+					val = _parse(line, 1)
+					layer[key] = val
+				except:
+					'banana ninja yadayada'
+
+	meta.update(layer) # last layer contains meta info
+	if 'anchors' in meta:
+		splits = meta['anchors'].split(',')
+		anchors = [float(x.strip()) for x in splits]
+		meta['anchors'] = anchors
+	meta['model'] = model # path to cfg, not model name
+	meta['inp_size'] = [h, w, c]
+	return layers, meta
+
+def cfg_yielder(model, binary):
+	"""
+	yielding each layer information to initialize `layer`
+	"""
+	layers, meta = parser(model); yield meta;
+	h, w, c = meta['inp_size']; l = w * h * c
+
+	# Start yielding
+	flat = False # flag for 1st dense layer
+	conv = '.conv.' in model
+	for i, d in enumerate(layers):
+		#-----------------------------------------------------
+		if d['type'] == '[crop]':
+			yield ['crop', i]
+		#-----------------------------------------------------
+		elif d['type'] == '[local]':
+			n = d.get('filters', 1)
+			size = d.get('size', 1)
+			stride = d.get('stride', 1)
+			pad = d.get('pad', 0)
+			activation = d.get('activation', 'logistic')
+			w_ = (w - 1 - (1 - pad) * (size - 1)) // stride + 1
+			h_ = (h - 1 - (1 - pad) * (size - 1)) // stride + 1
+			yield ['local', i, size, c, n, stride, 
+					pad, w_, h_, activation]
+			if activation != 'linear': yield [activation, i]
+			w, h, c = w_, h_, n
+			l = w * h * c
+		#-----------------------------------------------------
+		elif d['type'] == '[convolutional]':
+			n = d.get('filters', 1)
+			size = d.get('size', 1)
+			stride = d.get('stride', 1)
+			pad = d.get('pad', 0)
+			padding = d.get('padding', 0)
+			if pad: padding = size // 2
+			activation = d.get('activation', 'logistic')
+			batch_norm = d.get('batch_normalize', 0) or conv
+			yield ['convolutional', i, size, c, n, 
+				   stride, padding, batch_norm,
+				   activation]
+			if activation != 'linear': yield [activation, i]
+			w_ = (w + 2 * padding - size) // stride + 1
+			h_ = (h + 2 * padding - size) // stride + 1
+			w, h, c = w_, h_, n
+			l = w * h * c
+		#-----------------------------------------------------
+		elif d['type'] == '[maxpool]':
+			stride = d.get('stride', 1)
+			size = d.get('size', stride)
+			padding = d.get('padding', (size-1) // 2)
+			yield ['maxpool', i, size, stride, padding]
+			w_ = (w + 2*padding) // d['stride'] 
+			h_ = (h + 2*padding) // d['stride']
+			w, h = w_, h_
+			l = w * h * c
+		#-----------------------------------------------------
+		elif d['type'] == '[avgpool]':
+			flat = True; l = c
+			yield ['avgpool', i]
+		#-----------------------------------------------------
+		elif d['type'] == '[softmax]':
+			yield ['softmax', i, d['groups']]
+		#-----------------------------------------------------
+		elif d['type'] == '[connected]':
+			if not flat:
+				yield ['flatten', i]
+				flat = True
+			activation = d.get('activation', 'logistic')
+			yield ['connected', i, l, d['output'], activation]
+			if activation != 'linear': yield [activation, i]
+			l = d['output']
+		#-----------------------------------------------------
+		elif d['type'] == '[dropout]': 
+			yield ['dropout', i, d['probability']]
+		#-----------------------------------------------------
+		elif d['type'] == '[select]':
+			if not flat:
+				yield ['flatten', i]
+				flat = True
+			inp = d.get('input', None)
+			if type(inp) is str:
+				file = inp.split(',')[0]
+				layer_num = int(inp.split(',')[1])
+				with open(file, 'rb') as f:
+					profiles = pickle.load(f, encoding = 'latin1')[0]
+				layer = profiles[layer_num]
+			else: layer = inp
+			activation = d.get('activation', 'logistic')
+			d['keep'] = d['keep'].split('/')
+			classes = int(d['keep'][-1])
+			keep = [int(c) for c in d['keep'][0].split(',')]
+			keep_n = len(keep)
+			train_from = classes * d['bins']
+			for count in range(d['bins']-1):
+				for num in keep[-keep_n:]:
+					keep += [num + classes]
+			k = 1
+			while layers[i-k]['type'] not in ['[connected]', '[extract]']:
+				k += 1
+				if i-k < 0:
+					break
+			if i-k < 0: l_ = l
+			elif layers[i-k]['type'] == 'connected':
+				l_ = layers[i-k]['output']
+			else:
+				l_ = layers[i-k].get('old',[l])[-1]
+			yield ['select', i, l_, d['old_output'],
+				   activation, layer, d['output'], 
+				   keep, train_from]
+			if activation != 'linear': yield [activation, i]
+			l = d['output']
+		#-----------------------------------------------------
+		elif d['type'] == '[conv-select]':
+			n = d.get('filters', 1)
+			size = d.get('size', 1)
+			stride = d.get('stride', 1)
+			pad = d.get('pad', 0)
+			padding = d.get('padding', 0)
+			if pad: padding = size // 2
+			activation = d.get('activation', 'logistic')
+			batch_norm = d.get('batch_normalize', 0) or conv
+			d['keep'] = d['keep'].split('/')
+			classes = int(d['keep'][-1])
+			keep = [int(x) for x in d['keep'][0].split(',')]
+
+			segment = classes + 5
+			assert n % segment == 0, \
+			'conv-select: segment failed'
+			bins = n // segment
+			keep_idx = list()
+			for j in range(bins):
+				offset = j * segment
+				for k in range(5):
+					keep_idx += [offset + k]
+				for k in keep:
+					keep_idx += [offset + 5 + k]
+			w_ = (w + 2 * padding - size) // stride + 1
+			h_ = (h + 2 * padding - size) // stride + 1
+			c_ = len(keep_idx)
+			yield ['conv-select', i, size, c, n, 
+				   stride, padding, batch_norm,
+				   activation, keep_idx, c_]
+			w, h, c = w_, h_, c_
+			l = w * h * c
+		#-----------------------------------------------------
+		elif d['type'] == '[conv-extract]':
+			file = d['profile']
+			with open(file, 'rb') as f:
+				profiles = pickle.load(f, encoding = 'latin1')[0]
+			inp_layer = None
+			inp = d['input']
+			out = d['output']
+			inp_layer = None
+			if inp >= 0:
+				inp_layer = profiles[inp]
+			if inp_layer is not None:
+				assert len(inp_layer) == c, \
+				'Conv-extract does not match input dimension'
+			out_layer = profiles[out]
+
+			n = d.get('filters', 1)
+			size = d.get('size', 1)
+			stride = d.get('stride', 1)
+			pad = d.get('pad', 0)
+			padding = d.get('padding', 0)
+			if pad: padding = size // 2
+			activation = d.get('activation', 'logistic')
+			batch_norm = d.get('batch_normalize', 0) or conv
+			
+			k = 1
+			find = ['[convolutional]','[conv-extract]']
+			while layers[i-k]['type'] not in find:
+				k += 1
+				if i-k < 0: break
+			if i-k >= 0:
+				previous_layer = layers[i-k]
+				c_ = previous_layer['filters']
+			else:
+				c_ = c
+			
+			yield ['conv-extract', i, size, c_, n, 
+				   stride, padding, batch_norm,
+				   activation, inp_layer, out_layer]
+			if activation != 'linear': yield [activation, i]
+			w_ = (w + 2 * padding - size) // stride + 1
+			h_ = (h + 2 * padding - size) // stride + 1
+			w, h, c = w_, h_, len(out_layer)
+			l = w * h * c
+		#-----------------------------------------------------
+		elif d['type'] == '[extract]':
+			if not flat:
+				yield['flatten', i]
+				flat = True
+			activation = d.get('activation', 'logistic')
+			file = d['profile']
+			with open(file, 'rb') as f:
+				profiles = pickle.load(f, encoding = 'latin1')[0]
+			inp_layer = None
+			inp = d['input']
+			out = d['output']
+			if inp >= 0:
+				inp_layer = profiles[inp]
+			out_layer = profiles[out]
+			old = d['old']
+			old = [int(x) for x in old.split(',')]
+			if inp_layer is not None:
+				if len(old) > 2: 
+					h_, w_, c_, n_ = old
+					new_inp = list()
+					for p in range(c_):
+						for q in range(h_):
+							for r in range(w_):
+								if p not in inp_layer:
+									continue
+								new_inp += [r + w*(q + h*p)]
+					inp_layer = new_inp
+					old = [h_ * w_ * c_, n_]
+				assert len(inp_layer) == l, \
+				'Extract does not match input dimension'
+			d['old'] = old
+			yield ['extract', i] + old + [activation] + [inp_layer, out_layer]
+			if activation != 'linear': yield [activation, i]
+			l = len(out_layer)
+		#-----------------------------------------------------
+		elif d['type'] == '[route]': # add new layer here
+			routes = d['layers']
+			if type(routes) is int:
+				routes = [routes]
+			else:
+				routes = [int(x.strip()) for x in routes.split(',')]
+			routes = [i + x if x < 0 else x for x in routes]
+			for j, x in enumerate(routes):
+				lx = layers[x]; 
+				xtype = lx['type']
+				_size = lx['_size'][:3]
+				if j == 0:
+					h, w, c = _size
+				else: 
+					h_, w_, c_ = _size
+					assert w_ == w and h_ == h, \
+					'Routing incompatible conv sizes'
+					c += c_
+			yield ['route', i, routes]
+			l = w * h * c
+		#-----------------------------------------------------
+		elif d['type'] == '[reorg]':
+			stride = d.get('stride', 1)
+			yield ['reorg', i, stride]
+			w = w // stride; h = h // stride; 
+			c = c * (stride ** 2)
+			l = w * h * c
+		#-----------------------------------------------------
+		else:
+			exit('Layer {} not implemented'.format(d['type']))
+
+		d['_size'] = list([h, w, c, l, flat])
+
+	if not flat: meta['out_size'] = [h, w, c]
+	else: meta['out_size'] = l
\ No newline at end of file
diff --git a/darkflow/version.py b/darkflow/version.py
new file mode 100644
index 0000000..163f7d8
--- /dev/null
+++ b/darkflow/version.py
@@ -0,0 +1,2 @@
+__version__ = '1.0.0'
+"""Current version of darkflow."""
\ No newline at end of file
diff --git a/flow b/flow
new file mode 100644
index 0000000..e023989
--- /dev/null
+++ b/flow
@@ -0,0 +1,7 @@
+#! /usr/bin/env python
+
+import sys
+from darkflow.cli import cliHandler
+
+cliHandler(sys.argv)
+
diff --git a/labels.txt b/labels.txt
new file mode 100644
index 0000000..c42bfe3
--- /dev/null
+++ b/labels.txt
@@ -0,0 +1,4 @@
+aeroplane
+bicycle
+bird
+boat
\ No newline at end of file
diff --git a/preview.png b/preview.png
new file mode 100644
index 0000000..2827ee6
Binary files /dev/null and b/preview.png differ
diff --git a/sample_img/sample_computer.jpg b/sample_img/sample_computer.jpg
new file mode 100644
index 0000000..1a3b717
Binary files /dev/null and b/sample_img/sample_computer.jpg differ
diff --git a/sample_img/sample_dog.jpg b/sample_img/sample_dog.jpg
new file mode 100644
index 0000000..77b0381
Binary files /dev/null and b/sample_img/sample_dog.jpg differ
diff --git a/sample_img/sample_eagle.jpg b/sample_img/sample_eagle.jpg
new file mode 100644
index 0000000..8b75095
Binary files /dev/null and b/sample_img/sample_eagle.jpg differ
diff --git a/sample_img/sample_giraffe.jpg b/sample_img/sample_giraffe.jpg
new file mode 100644
index 0000000..a93e8b8
Binary files /dev/null and b/sample_img/sample_giraffe.jpg differ
diff --git a/sample_img/sample_horses.jpg b/sample_img/sample_horses.jpg
new file mode 100644
index 0000000..3a761f4
Binary files /dev/null and b/sample_img/sample_horses.jpg differ
diff --git a/sample_img/sample_office.jpg b/sample_img/sample_office.jpg
new file mode 100644
index 0000000..fef4f53
Binary files /dev/null and b/sample_img/sample_office.jpg differ
diff --git a/sample_img/sample_person.jpg b/sample_img/sample_person.jpg
new file mode 100644
index 0000000..61d377f
Binary files /dev/null and b/sample_img/sample_person.jpg differ
diff --git a/sample_img/sample_scream.jpg b/sample_img/sample_scream.jpg
new file mode 100644
index 0000000..43f2c36
Binary files /dev/null and b/sample_img/sample_scream.jpg differ
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..d3d255e
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,74 @@
+from setuptools import setup, find_packages
+from setuptools.extension import Extension
+from Cython.Build import cythonize
+import numpy
+import os
+import imp
+
+VERSION = imp.load_source('version', os.path.join('.', 'darkflow', 'version.py'))
+VERSION = VERSION.__version__
+
+if os.name =='nt' :
+    ext_modules=[
+        Extension("darkflow.cython_utils.nms",
+            sources=["darkflow/cython_utils/nms.pyx"],
+            #libraries=["m"] # Unix-like specific
+            include_dirs=[numpy.get_include()]
+        ),        
+        Extension("darkflow.cython_utils.cy_yolo2_findboxes",
+            sources=["darkflow/cython_utils/cy_yolo2_findboxes.pyx"],
+            #libraries=["m"] # Unix-like specific
+            include_dirs=[numpy.get_include()]
+        ),
+        Extension("darkflow.cython_utils.cy_yolo_findboxes",
+            sources=["darkflow/cython_utils/cy_yolo_findboxes.pyx"],
+            #libraries=["m"] # Unix-like specific
+            include_dirs=[numpy.get_include()]
+        )
+    ]
+
+elif os.name =='posix' :
+    ext_modules=[
+        Extension("darkflow.cython_utils.nms",
+            sources=["darkflow/cython_utils/nms.pyx"],
+            libraries=["m"], # Unix-like specific
+            include_dirs=[numpy.get_include()]
+        ),        
+        Extension("darkflow.cython_utils.cy_yolo2_findboxes",
+            sources=["darkflow/cython_utils/cy_yolo2_findboxes.pyx"],
+            libraries=["m"], # Unix-like specific
+            include_dirs=[numpy.get_include()]
+        ),
+        Extension("darkflow.cython_utils.cy_yolo_findboxes",
+            sources=["darkflow/cython_utils/cy_yolo_findboxes.pyx"],
+            libraries=["m"], # Unix-like specific
+            include_dirs=[numpy.get_include()]
+        )
+    ]
+
+else :
+    ext_modules=[
+        Extension("darkflow.cython_utils.nms",
+            sources=["darkflow/cython_utils/nms.pyx"],
+            libraries=["m"] # Unix-like specific
+        ),        
+        Extension("darkflow.cython_utils.cy_yolo2_findboxes",
+            sources=["darkflow/cython_utils/cy_yolo2_findboxes.pyx"],
+            libraries=["m"] # Unix-like specific
+        ),
+        Extension("darkflow.cython_utils.cy_yolo_findboxes",
+            sources=["darkflow/cython_utils/cy_yolo_findboxes.pyx"],
+            libraries=["m"] # Unix-like specific
+        )
+    ]
+
+setup(
+    version=VERSION,
+	name='darkflow',
+    description='Darkflow',
+    license='GPLv3',
+    url='https://github.com/thtrieu/darkflow',
+    packages = find_packages(),
+	scripts = ['flow'],
+    ext_modules = cythonize(ext_modules)
+)
\ No newline at end of file
diff --git a/test/requirements-testing.txt b/test/requirements-testing.txt
new file mode 100644
index 0000000..5c73070
--- /dev/null
+++ b/test/requirements-testing.txt
@@ -0,0 +1,8 @@
+tensorflow
+pytest
+requests
+opencv-python
+numpy
+Cython
+codecov
+pytest-cov
\ No newline at end of file
diff --git a/test/test_darkflow.py b/test/test_darkflow.py
new file mode 100644
index 0000000..b3ed38c
--- /dev/null
+++ b/test/test_darkflow.py
@@ -0,0 +1,220 @@
+from darkflow.net.build import TFNet
+from darkflow.cli import cliHandler
+import json
+import requests
+import cv2
+import os
+import sys
+import pytest
+
+#NOTE: This file is designed to be run in the TravisCI environment. If you want to run it locally set the environment variable TRAVIS_BUILD_DIR to the base
+#      directory of the cloned darkflow repository. WARNING: This file delete images from sample_img/ that won't be used for testing (so don't run it
+#      locally if you don't want this happening!)
+
+#Settings
+buildPath = os.environ.get("TRAVIS_BUILD_DIR")
+
+if buildPath is None:
+    print()
+    print("TRAVIS_BUILD_DIR environment variable was not found - is this running on TravisCI?")
+    print("If you want to test this locally, set TRAVIS_BUILD_DIR to the base directory of the cloned darkflow repository.")
+    exit()
+
+testImg = {"path": os.path.join(buildPath, "sample_img", "sample_person.jpg"), "width": 640, "height": 424,
+           "expected-objects": {"yolo-small": [{"label": "dog", "confidence": 0.46, "topleft": {"x": 84, "y": 249}, "bottomright": {"x": 208, "y": 367}},
+                                                  {"label": "person", "confidence": 0.60, "topleft": {"x": 159, "y": 102}, "bottomright": {"x": 304, "y": 365}}],
+                                "yolo":       [{"label": "person", "confidence": 0.82, "topleft": {"x": 189, "y": 96}, "bottomright": {"x": 271, "y": 380}},
+                                                  {"label": "dog", "confidence": 0.79, "topleft": {"x": 69, "y": 258}, "bottomright": {"x": 209, "y": 354}},
+                                                  {"label": "horse", "confidence": 0.89, "topleft": {"x": 397, "y": 127}, "bottomright": {"x": 605, "y": 352}}]}}
+
+trainImgBikePerson = {"path": os.path.join(buildPath, "test", "training", "images", "1.jpg"), "width": 500, "height": 375,
+                      "expected-objects": {"tiny-yolo-voc": [{"label":"bicycle","confidence":0.35,"topleft":{"x":121,"y":126},"bottomright":{"x":233,"y":244}},
+                                                             {"label":"person","confidence":0.60,"topleft":{"x":132,"y":35},"bottomright":{"x":232,"y":165}}]}}
+
+trainImgHorsePerson = {"path": os.path.join(buildPath, "test", "training", "images", "2.jpg"), "width": 500, "height": 332,
+                       "expected-objects": {"tiny-yolo-voc": [{"label":"horse","confidence":0.99,"topleft":{"x":156,"y":108},"bottomright":{"x":410,"y":281}},
+                                                              {"label":"person","confidence":0.89,"topleft":{"x":258,"y":52},"bottomright":{"x":300,"y":218}}]}}
+
+
+posCompareThreshold = 0.05 #Comparisons must match be within 5% of width/height when compared to expected value
+threshCompareThreshold = 0.1 #Comparisons must match within 0.1 of expected threshold for each prediction
+
+yolo_small_Download = "https://pjreddie.com/media/files/yolo-small.weights" #YOLOv1
+yolo_Download = "https://pjreddie.com/media/files/yolo.weights" #YOLOv2
+tiny_yolo_voc_Download = "https://pjreddie.com/media/files/tiny-yolo-voc.weights" #YOLOv2
+
+def download_file(url, savePath):
+    fileName = savePath.split("/")[-1]
+    if not os.path.isfile(savePath):
+        os.makedirs(os.path.dirname(savePath), exist_ok=True) #Make directories nessecary for file incase they don't exist
+        print("Downloading " + fileName + " file...")
+        r = requests.get(url, stream=True)
+        with open(savePath, 'wb') as f:
+            for chunk in r.iter_content(chunk_size=1024): 
+                if chunk: # filter out keep-alive new chunks
+                    f.write(chunk)
+        r.close()
+    else:
+        print("Found existing " + fileName + " file.")
+
+yolo_small_WeightPath = os.path.join(buildPath, "bin", yolo_small_Download.split("/")[-1])
+yolo_small_CfgPath = os.path.join(buildPath, "cfg", "v1", "{0}.cfg".format(os.path.splitext(os.path.basename(yolo_small_WeightPath))[0]))
+
+yolo_WeightPath = os.path.join(buildPath, "bin", yolo_Download.split("/")[-1])
+yolo_CfgPath = os.path.join(buildPath, "cfg", "{0}.cfg".format(os.path.splitext(os.path.basename(yolo_WeightPath))[0]))
+
+tiny_yolo_voc_WeightPath = os.path.join(buildPath, "bin", tiny_yolo_voc_Download.split("/")[-1])
+tiny_yolo_voc_CfgPath = os.path.join(buildPath, "cfg", "{0}.cfg".format(os.path.splitext(os.path.basename(tiny_yolo_voc_WeightPath))[0]))
+
+pbPath = os.path.join(buildPath, "built_graph", os.path.splitext(os.path.basename(yolo_WeightPath))[0] + ".pb")
+metaPath = os.path.join(buildPath, "built_graph", os.path.splitext(os.path.basename(yolo_WeightPath))[0] + ".meta")
+
+generalConfigPath = os.path.join(buildPath, "cfg")
+
+download_file(yolo_small_Download, yolo_small_WeightPath) #Check if we need to download (and if so download) the yolo-small weights (YOLOv1)
+download_file(yolo_Download, yolo_WeightPath) #Check if we need to download (and if so download) the yolo weights (YOLOv2)
+download_file(tiny_yolo_voc_Download, tiny_yolo_voc_WeightPath) #Check if we need to download (and if so download) the tiny-yolo-voc weights (YOLOv2)
+
+def executeCLI(commandString):
+    print()
+    print("Executing: {0}".format(commandString))
+    print()
+    splitArgs = [item.strip() for item in commandString.split(" ")]
+    cliHandler(splitArgs) #Run the command
+    print()
+
+def compareSingleObjects(firstObject, secondObject, width, height, threshCompare, posCompare):
+    if(firstObject["label"] != secondObject["label"]):
+        return False
+    if(abs(firstObject["topleft"]["x"] - secondObject["topleft"]["x"]) > width * posCompare):
+        return False
+    if(abs(firstObject["topleft"]["y"] - secondObject["topleft"]["y"]) > height * posCompare):
+        return False
+    if(abs(firstObject["bottomright"]["x"] - secondObject["bottomright"]["x"]) > width * posCompare):
+        return False
+    if(abs(firstObject["bottomright"]["y"] - secondObject["bottomright"]["y"]) > height * posCompare):
+        return False
+    if(abs(firstObject["confidence"] - secondObject["confidence"]) > threshCompare):
+        return False
+    return True
+
+def compareObjectData(defaultObjects, newObjects, width, height, threshCompare, posCompare):
+    currentlyFound = False
+    for firstObject in defaultObjects:
+        currentlyFound = False
+        for secondObject in newObjects:
+            if compareSingleObjects(firstObject, secondObject, width, height, threshCompare, posCompare):
+                currentlyFound = True
+                break
+        if not currentlyFound:
+            return False
+    return True
+
+#Delete all images that won't be tested on so forwarding the whole folder doesn't take forever
+filelist = [f for f in os.listdir(os.path.dirname(testImg["path"])) if os.path.isfile(os.path.join(os.path.dirname(testImg["path"]), f)) and f != os.path.basename(testImg["path"])]
+for f in filelist:
+    os.remove(os.path.join(os.path.dirname(testImg["path"]), f))
+
+
+#TESTS FOR INFERENCE
+def test_CLI_IMG_YOLOv2():
+    #Test predictions outputted to an image using the YOLOv2 model through CLI
+    #NOTE: This test currently does not verify anything about the image created (i.e. proper labeling, proper positioning of prediction boxes, etc.)
+    #      it simply verifies that the code executes properly and that the expected output image is indeed created in ./test/img/out
+
+    testString = "flow --imgdir {0} --model {1} --load {2} --config {3} --threshold 0.4".format(os.path.dirname(testImg["path"]), yolo_CfgPath, yolo_WeightPath, generalConfigPath)
+    executeCLI(testString)
+
+    outputImgPath = os.path.join(os.path.dirname(testImg["path"]), "out", os.path.basename(testImg["path"]))
+    assert os.path.exists(outputImgPath), "Expected output image: {0} was not found.".format(outputImgPath)
+    os.remove(outputImgPath) #Remove the image so that it does not affect subsequent tests
+
+def test_CLI_JSON_YOLOv2():
+    #Test predictions outputted to a JSON file using the YOLOv2 model through CLI
+    #NOTE: This test verifies that the code executes properly, the JSON file is created properly and the predictions generated are within a certain
+    #      margin of error when compared to the expected predictions.
+
+    testString = "flow --imgdir {0} --model {1} --load {2} --config {3} --threshold 0.4 --json".format(os.path.dirname(testImg["path"]), yolo_CfgPath, yolo_WeightPath, generalConfigPath)
+    executeCLI(testString)
+
+    outputJSONPath = os.path.join(os.path.dirname(testImg["path"]), "out", os.path.splitext(os.path.basename(testImg["path"]))[0] + ".json")
+    assert os.path.exists(outputJSONPath), "Expected output JSON file: {0} was not found.".format(outputJSONPath)
+
+    with open(outputJSONPath) as json_file:
+        loadedPredictions = json.load(json_file)
+
+    assert compareObjectData(testImg["expected-objects"]["yolo"], loadedPredictions, testImg["width"], testImg["height"], threshCompareThreshold, posCompareThreshold), "Generated object predictions from JSON were not within margin of error compared to expected values."
+    os.remove(outputJSONPath) #Remove the JSON file so that it does not affect subsequent tests
+
+def test_CLI_JSON_YOLOv1():
+    #Test predictions outputted to a JSON file using the YOLOv1 model through CLI
+    #NOTE: This test verifies that the code executes properly, the JSON file is created properly and the predictions generated are within a certain
+    #      margin of error when compared to the expected predictions.
+
+    testString = "flow --imgdir {0} --model {1} --load {2} --config {3} --threshold 0.4 --json".format(os.path.dirname(testImg["path"]), yolo_small_CfgPath, yolo_small_WeightPath, generalConfigPath)
+    executeCLI(testString)
+
+    outputJSONPath = os.path.join(os.path.dirname(testImg["path"]), "out", os.path.splitext(os.path.basename(testImg["path"]))[0] + ".json")
+    assert os.path.exists(outputJSONPath), "Expected output JSON file: {0} was not found.".format(outputJSONPath)
+
+    with open(outputJSONPath) as json_file:
+        loadedPredictions = json.load(json_file)
+
+    assert compareObjectData(testImg["expected-objects"]["yolo-small"], loadedPredictions, testImg["width"], testImg["height"], threshCompareThreshold, posCompareThreshold), "Generated object predictions from JSON were not within margin of error compared to expected values."
+    os.remove(outputJSONPath) #Remove the JSON file so that it does not affect subsequent tests
+
+def test_CLI_SAVEPB_YOLOv2():
+    #Save .pb and .meta as generated from the YOLOv2 model through CLI
+    #NOTE: This test verifies that the code executes properly, and the .pb and .meta files are successfully created. The subsequent test will verify the
+    #      contents of those files.
+
+    testString = "flow --model {0} --load {1} --config {2} --threshold 0.4 --savepb".format(yolo_CfgPath, yolo_WeightPath, generalConfigPath)
+    
+    with pytest.raises(SystemExit):
+            executeCLI(testString)
+
+    assert os.path.exists(pbPath), "Expected output .pb file: {0} was not found.".format(pbPath)
+    assert os.path.exists(metaPath), "Expected output .meta file: {0} was not found.".format(metaPath)
+
+def test_RETURNPREDICT_PBLOAD_YOLOv2():
+    #Test the .pb and .meta files generated in the previous step
+    #NOTE: This test verifies that the code executes properly, and the .pb and .meta files that were created are able to be loaded and used for inference.
+    #      The predictions that are generated will be compared against expected predictions.
+
+    options = {"pbLoad": pbPath, "metaLoad": metaPath, "threshold": 0.4}
+    tfnet = TFNet(options)
+    imgcv = cv2.imread(testImg["path"])
+    loadedPredictions = tfnet.return_predict(imgcv)
+
+    assert compareObjectData(testImg["expected-objects"]["yolo"], loadedPredictions, testImg["width"], testImg["height"], threshCompareThreshold, posCompareThreshold), "Generated object predictions from return_predict() were not within margin of error compared to expected values."
+
+#TESTS FOR TRAINING
+def test_TRAIN_FROM_WEIGHTS_CLI__LOAD_CHECKPOINT_RETURNPREDICT_YOLOv2():
+    #Test training using pre-generated weights for tiny-yolo-voc
+    #NOTE: This test verifies that the code executes properly, and that the expected checkpoint file (tiny-yolo-voc-20.meta in this case) is generated.
+    #      In addition, predictions are generated using the checkpoint file to verify that training completed successfully.
+
+    testString = "flow --model {0} --load {1} --train --dataset {2} --annotation {3} --epoch 20".format(tiny_yolo_voc_CfgPath, tiny_yolo_voc_WeightPath, os.path.join(buildPath, "test", "training", "images"), os.path.join(buildPath, "test", "training", "annotations"))
+    with pytest.raises(SystemExit):
+        executeCLI(testString)
+
+    checkpointPath = os.path.join(buildPath, "ckpt", "tiny-yolo-voc-20.meta")
+    assert os.path.exists(checkpointPath), "Expected output checkpoint file: {0} was not found.".format(checkpointPath)
+
+    #Using trained weights
+    options = {"model": tiny_yolo_voc_CfgPath, "load": 20, "config": generalConfigPath, "threshold": 0.1}
+    tfnet = TFNet(options)
+
+    #Make sure predictions very roughly match the expected values for image with bike and person
+    imgcv = cv2.imread(trainImgBikePerson["path"])
+    loadedPredictions = tfnet.return_predict(imgcv)
+    assert compareObjectData(trainImgBikePerson["expected-objects"]["tiny-yolo-voc"], loadedPredictions, trainImgBikePerson["width"], trainImgBikePerson["height"], 0.7, 0.25), "Generated object predictions from training (for image with person on the bike) were not anywhere close to what they are expected to be.\nTraining may not have completed successfully."
+    differentThanExpectedBike = compareObjectData(trainImgBikePerson["expected-objects"]["tiny-yolo-voc"], loadedPredictions, trainImgBikePerson["width"], trainImgBikePerson["height"], 0.01, 0.001)
+
+    #Make sure predictions very roughly match the expected values for image with horse and person
+    imgcv = cv2.imread(trainImgHorsePerson["path"])
+    loadedPredictions = tfnet.return_predict(imgcv)
+    assert compareObjectData(trainImgHorsePerson["expected-objects"]["tiny-yolo-voc"], loadedPredictions, trainImgHorsePerson["width"], trainImgHorsePerson["height"], 0.7, 0.25), "Generated object predictions from training (for image with person on the horse) were not anywhere close to what they are expected to be.\nTraining may not have completed successfully."
+    differentThanExpectedHorse = compareObjectData(trainImgHorsePerson["expected-objects"]["tiny-yolo-voc"], loadedPredictions, trainImgHorsePerson["width"], trainImgHorsePerson["height"], 0.01, 0.001)
+
+    assert not (differentThanExpectedBike and differentThanExpectedHorse), "The generated object predictions for both images appear to be exactly the same as the ones generated with the original weights.\nTraining may not have completed successfully.\n\nNOTE: It is possible this is a fluke error and training did complete properly (try running this build again to confirm) - but most likely something is wrong."
\ No newline at end of file
diff --git a/test/training/annotations/1.xml b/test/training/annotations/1.xml
new file mode 100644
index 0000000..1700745
--- /dev/null
+++ b/test/training/annotations/1.xml
@@ -0,0 +1,44 @@
+<annotation>
+	<folder>VOC2007</folder>
+	<filename>1.jpg</filename>
+	<source>
+		<database>The VOC2007 Database</database>
+		<annotation>PASCAL VOC2007</annotation>
+		<image>flickr</image>
+		<flickrid>336426776</flickrid>
+	</source>
+	<owner>
+		<flickrid>Elder Timothy Chaves</flickrid>
+		<name>Tim Chaves</name>
+	</owner>
+	<size>
+		<width>500</width>
+		<height>375</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>person</name>
+		<pose>Left</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>135</xmin>
+			<ymin>25</ymin>
+			<xmax>236</xmax>
+			<ymax>188</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>bicycle</name>
+		<pose>Left</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>95</xmin>
+			<ymin>85</ymin>
+			<xmax>232</xmax>
+			<ymax>253</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/test/training/annotations/2.xml b/test/training/annotations/2.xml
new file mode 100644
index 0000000..c38f904
--- /dev/null
+++ b/test/training/annotations/2.xml
@@ -0,0 +1,44 @@
+<annotation>
+	<folder>VOC2007</folder>
+	<filename>2.jpg</filename>
+	<source>
+		<database>The VOC2007 Database</database>
+		<annotation>PASCAL VOC2007</annotation>
+		<image>flickr</image>
+		<flickrid>329950741</flickrid>
+	</source>
+	<owner>
+		<flickrid>Lothar Lenz</flickrid>
+		<name>Lothar Lenz</name>
+	</owner>
+	<size>
+		<width>500</width>
+		<height>332</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>person</name>
+		<pose>Left</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>235</xmin>
+			<ymin>51</ymin>
+			<xmax>309</xmax>
+			<ymax>222</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>horse</name>
+		<pose>Left</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>157</xmin>
+			<ymin>106</ymin>
+			<xmax>426</xmax>
+			<ymax>294</ymax>
+		</bndbox>
+	</object>
+</annotation>
diff --git a/test/training/images/1.jpg b/test/training/images/1.jpg
new file mode 100644
index 0000000..3d42943
Binary files /dev/null and b/test/training/images/1.jpg differ
diff --git a/test/training/images/2.jpg b/test/training/images/2.jpg
new file mode 100644
index 0000000..1ee3cd5
Binary files /dev/null and b/test/training/images/2.jpg differ